ethics Archives - Littal Shemer Haim https://www.littalics.com/tag/ethics/ People Analytics, HR Data Strategy, Organizational Research - Consultant, Mentor, Speaker, Influencer Thu, 14 Mar 2024 15:32:46 +0000 en hourly 1 https://wordpress.org/?v=6.7.2 https://www.littalics.com/wp-content/uploads/2021/02/cropped-grey-32x32.png ethics Archives - Littal Shemer Haim https://www.littalics.com/tag/ethics/ 32 32 Ethics in People Analytics and AI at Work – Best Resources https://www.littalics.com/ethics-in-people-analytics-and-ai-at-work-best-resources-discovered-monthly/ Fri, 01 Jan 2021 07:00:00 +0000 https://www.littalics.com/?p=2850 Part of my continuous learning, collaboration, and contribution is a comprehensive resource list, updated monthly. It includes four categories: strategic thinking, practical advice, product reviews, and a social context.

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Ethics in People Analytics and AI at Work
Best Resources Discovered Monthly

Edition #7 – December 2020

There is a severe knowledge gap. Business leaders’ and HR practitioners’ quantitative abilities are based on the descriptive or inferential statistics that we all learned. Machine learning is entirely different. To understand it and evaluate it to the level of dealing with potential risks, let alone algorithm auditing, a systematic approach and a practical methodology is needed.

Part of my continuous learning, collaboration, and contribution, which hopefully lead to an articulation of a solution for evaluating the Ethics of workforce AI, is a comprehensive resource list that will be updated monthly. For now, I decided to include four categories in it: strategic thinking, practical advice, product reviews, and a social context.

Why these categories? I hope that such a categorization will facilitate learning in the field. Particularly, leaders need to understand how to incorporate questions about values in their businesses, starting in their strategic planning. Then, they may need a helping hand to translate those values and plans into daily practices and procedures. Those practices can be demonstrated in discussions and reviews about specific products. But at the end of the day, business leaders influence the employees, their families, their communities, and society. Therefore, this resource list must include a social perspective too.

 

Workforce AI Ethics in strategic thinking

Navigate the road to Responsible AI

Ben Lorica

The practice of Responsible AI encompasses more than just privacy and security. It also includes concerns around safety and reliability, fairness, transparency, and accountability. The breadth and depth of domain knowledge required to address those disparate areas mean that deploying AI ethically and responsibly will involve cross-functional team collaboration, new tools and processes, and proper support from key stakeholders.

The recent regulatory changes (required in GDPR and CCPA) prioritized privacy, security, and transparency principles. However, a shift in Responsible AI priorities is reflected in surveys. Results confirmed that security and transparency were indeed the top two principles executives intend to address, but many indicate that fairness—or testing for bias—has become a top priority. To develop tools around these ethical principles, stakeholders will need to agree on precise definitions of each. Organizations need to establish a clear understanding of the limitations of the tools they are using. They need to learn how to match models and techniques to their specific problems and challenges.

Organizations are still reactive in regards to AI. They use revenue-generating measurements without adequately addressing ethical issues. Effective Responsible AI should integrate and implement the principles as early in the product development process as possible. The inclusion of Responsible AI principles should also be routine, and part of the production culture. One of the main challenges is that current measuring business success methods don’t translate to measuring of Responsible AI successful implementations. Key performance indicators (KPIs) for business are very different from academic benchmarks, and traditional quantitative business metrics aren’t designed to encompass the qualitative aspects of Responsible AI principles.

Workforce AI Ethics in practical advice

Design of hiring algorithms can double diversity in firms

Danielle Li

Automated approaches codify existing human biases to the detriment of candidates from underrepresented groups. Hiring algorithms use the information on employees to predict which job applicants they should select. In many cases, relying on such algorithms that predict future success based on past success leads to favor applicants from groups that have traditionally been successful.

Instead of designing algorithms that view hiring as a static prediction problem, researchers suggest designing algorithms that consider the challenge of finding the best job applicants as a continual learning process. In a recent study, the authors developed and evaluated hiring algorithms designed to explicitly value exploration to learn about people who might not have been previously considered for jobs. The algorithm incorporated exploration bonuses that increase its degree of uncertainty about quality in the case of underrepresented candidates. For example, such cases could be applicants with unusual majors, applicants who attended less common colleges, applicants with different types of work histories, and applicants who are demographically underrepresented at the firm.

Research reveals significant differences in the candidates selected by the exploratory versus static algorithms, i.e., a higher share of selected applicants among minorities. The overall findings are clear: “When you incorporate exploration into the algorithm, you improve the quality of talent and hire more diverse candidates. Firms that continue to use static approaches in their algorithms risk missing out on quality applicants from different backgrounds.”

Workforce AI Ethics in product reviews

World first for ethical AI and workplace equity

University of Technology Sydney

A workforce intelligence platform partnered with the University of Technology Sydney to deliver non-biased talent shortlisting algorithm validation. The project was a pioneering independent validation of Ethical AI. The research team has developed, tested, and iterated the ground-breaking assessment process before its use by industry partners to confirm that the AI outputs are fit for purpose and deliver actionable results.

Workforce AI deals with sensitive information about real people, so building trust in that process is critical. AI for good needs to be the standard. However, there has been no way to assess that properly. AI is not immune to bias in the data or the algorithms. Previously, the decision making has been hidden in a black box, and there has been no clear, defensible, independent, and objective validation demonstrating ethical AI. There are over 200 AI ethics frameworks and guidelines globally, few have been operationalized, and this project is a milestone in bringing audited certification to an innovative AI product independently.

Reejig uses big data and verified AI to help organizations understand and analyze their talent ecosystem skills and capabilities. It connects existing HR systems, cleanses and aggregates talent data, and unifies data across the enterprise. This, coupled with market, industry, and competitor intelligence and skills mapping, helps companies design their workforce of the future. The platform is automatically matching potential candidates or employees to opportunities to remove negative unconscious bias from the process and assist the HR users in explaining why talent has been recommended to ensure it complies with Equal Opportunities and employment law.

Workforce AI Ethics in a social context

Europe is adopting stricter rules on surveillance tech

Patrick Howell O’Neill

The European Union will stricter rules on cyber-surveillance technologies like facial recognition and spyware. The new regulation requires companies to get a government license to sell technology with military applications. The main achievement is more transparency.

Governments must either disclose the destination, items, value, and licensing decisions for cyber-surveillance exports or make public the decision not to disclose those details. The regulation also includes guidance to “consider the risk of use in connection with internal repression or the commission of serious violations of international human rights and international humanitarian law.” The regulation’s effectiveness will depend on Europe’s national governments, which will be responsible for much of the implementation.

The new regulation mentions some specific surveillance tools, but it’s written to be more flexible and expansive. Still, how the rules are actually applied remains to be seen. Another obvious weakness of the new regulation is that it only covers EU member states. There’s an aim to create a global coalition of democracies willing to control the export of surveillance technologies more tightly. The reform makes sense. However, this regulation is only the beginning.

Previous Editions

Edition #6 – November 2020

Edition #5 – September 2020

Edition #4 – September 2020

Edition #3 – August 2020

Edition #2 – July 2020

Edition #1 – June 2020

Edition #6 – November 2020

Workforce AI Ethics in strategic thinking

Ethical Frameworks for AI Aren’t Enough

Andrew Burt

As organizations embrace AI with increasing speed, adopting ethical principles is widely viewed as one of the best ways to ensure AI does not cause unintended harm. However, ethical frameworks cannot be clearly implemented in practice, as there’s not much technical personnel that can offer high-level guidance.

It means that AI ethics frameworks remain good marketing campaigns, more than preventing AI from causing harm. To ensure these frameworks are developed and implemented, every AI ethics principle that an organization adopts should have clear metrics.

There is no one-size-fits-all approach to quantifying potential harms created by AI. Therefore, metrics for ethical AI vary across organizations, use cases, and regulatory jurisdictions. Yet, each can be drawn from a combination of existing research, legal precedents, and technical best practices. The article offers some resources, methods, and examples of metrics for fairness, privacy. Indeed, organizations don’t need to start from scratch, but they do need to measure AI’s potential harms before they occur.

Workforce AI Ethics in practical advice

How to Monitor Remote Workers — Ethically

Ben Laker, Will Godley, Charmi Patel, and David Cobb

Long-term remote work has necessitated questions about monitoring employee productivity. Is it possible to practice ethical surveillance? While 88% of organizations worldwide now either encourage or require their employees to work from home, resulting in productivity improvements across 77% of the workforce, there is an alarming surge in monitoring employee activity.

Thousands of companies started panic-buying surveillance software, take webcam pictures of their employees, and monitor their screenshots, login times, and keystrokes, disclosed and legally. Workers’ concerns about privacy and security are not the only issue. Surveillance tools may reduce productivity for those who don’t feel trusted and may find creative ways to evade anti-surveillance software.

Recent research reveals some answers for ethical employee monitoring. It identifies five fundamental steps that companies should take: Accept that remote work is here to stay; Engage the workforce to reach agreement on which business activities actually require monitoring and ensure that the benefits of doing so are understood; Ensure that sufficient safeguards are introduced to prevent abuse; Be aware that discrimination can occur despite precautions put in place; Rebuild the trust levels that existed in office settings. The authors also advise setting goals and communicating expected outcomes, offering employees greater autonomy, collaborating tools, and channels to share presences.

Workforce AI Ethics in product reviews

This company embeds microchips in its employees, and they love it

Rachel Metz

This article explores the story of employees who volunteered to have a chip injected into their hands. The chip enables them to initiate activities by a hand wave, e.g., get into the office, log on to computers, and buy drinks in the company cafeteria.

The chips are about the size of a very large grain of rice. They don’t have batteries and instead get their power from an RFID (Radio Frequency Identification) reader when it requests data from the chip. User testimonials indicate that people get used to the chip as part of their routine, and most don’t want to remove it. Usage frequencies may reach 10-15 times a day.

Only some of the information stored on the chip is encrypted. Therefore, privacy and security of the data stored on the chips are obviously a concern regarding personal behavior and other use cases of employee behavior, e.g., monitoring hand washes of medical personnel. There’s also an issue or chance that the technology inside the employees’ bodies will become outdated. There’s a need for some upgrade program.

Workforce AI Ethics in a social context

The true dangers of AI are closer than we think

Karen Hao

AI is now screening job candidates, diagnosing disease, and identifying criminal suspects. But instead of making these decisions more efficient or fair, it’s often perpetuating the humans’ biases on whose decisions it was trained. Some AI ethical challenges and solutions were reviewed in an interview with William Isaac, who cochairs the Fairness, Accountability, and Transparency conference—the premier annual gathering of AI experts, social scientists, and lawyers working in this area.

According to Isaac, there are three challenges. First, there is a question about value alignment: how to design a system that can understand and implement various preferences and values of a population? Secondly, there are still a few empirical evidence that validates that AI technologies will achieve broad-based social benefit. Lastly, the biggest question is, what are the robust mechanisms of oversight and accountability. To overcome these risks, three are three areas. First, building a collective muscle for responsible innovation and oversight ensures all groups are engaged in the process of technological design. Secondly, accelerating the development of the sociotechnical tools actually to do this work. The last area is providing more funding and training for researchers and practitioners to conduct this work.

Edition #5 – October 2020

Workforce AI Ethics in strategic thinking

Artificial Intelligence (AI) Ethics: Ethics of AI and Ethical AI

Keng Siau

Artificial Intelligence-based technology has many achievements, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, human well-being, and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies.

Addressing the ethical and moral challenges associated with AI is critical as AI advances. However, AI Ethics, i.e., the field related to the study of ethical issues in AI, is still in its infancy stage. To address AI Ethics, the author distinguish between the Ethics of AI and how to build Ethical AI.

Ethics of AI studies the ethical principles, rules, guidelines, policies, and regulations related to AI. Ethical AI is an AI that performs and behaves ethically. The potential ethical and moral issues that AI may cause must be recognized and understood to formulate the necessary ethical principles, rules, guidelines, policies, and regulations for AI, i.e., Ethics of AI. With the appropriate Ethics of AI, AI that exhibits ethical behavior, i.e., Ethical AI, can be built.

Workforce AI Ethics in practical advice

A Practical Guide to Building Ethical AI

Reid Blackman

This Practical Guide to Building Ethical AI points to reasons for failure in standard approaches to AI Ethical risk mitigation, such as the academic approach, an on-the-ground approach, and embracing only high-level AI ethics principles. It offers seven steps towards building a customized, operationalized, scalable, and sustainable data and AI ethics program.

Until recently, the discussions of AI Ethics were reserved for nonprofit organizations and academics. Today the biggest tech companies are putting together fast-growing teams to tackle the ethical problems that arise from the widespread collection, analysis, and use of massive troves of data, mainly when that data is used to train machine learning models. Failing to operationalize AI Ethics is a threat to every company’s bottom line due to reputation, regulation, and legal risks. It might also lead to wasted resources, inefficiencies in product development and deployment, and even an inability to use data to train AI models at all.

When handling AI Ethics through ad-hoc discussions on a per-product basis, with no clear protocol in place to identify, evaluate, and mitigate the risks, companies end up overlooking risks. AI ethics programs must be tailored to the business and the relevant regulatory needs. However, there are recommended steps towards building a customized, operationalized, scalable, and sustainable AI Ethics program: 1. Identify existing infrastructure that a data and AI ethics program can leverage; 2. Create data and AI ethical risk framework that is tailored to your industry; 3. Change how you think about ethics by taking cues from the successes in health care; 4. Optimize guidance and tools for product managers; 5. Build organizational awareness; 6. Formally and informally incentivize employees to play a role in identifying AI ethical risks; 7. Monitor impacts and engage stakeholders.

Workforce AI Ethics in product reviews

Tech Is Transforming People Analytics. Is That a Good Thing?

Tomas Chamorro-Premuzic and Ian Bailie

The volume of data available to understand and predict employees’ behaviors will continue to grow exponentially, enabling more opportunities for managing through tech and data. However, this article questions the good consequences of advanced technology in People Analytics. People analytics is a deliberate and systematic attempt to make organizations more evidence-based. It summarizes this domain’s technology development, including employee listening tools, technologies used to monitor safety and well-being, biometric data people willingly shared to assess Covid-19 risk, performance or productivity boosters, and more.

The “creepy” monitoring factor starts to kick in, as phones, sensors, wearables, and IoT detect and record our moves. When such tools become mandatory, employees may worry about their privacy and the usage of their data for purposes other than Covid19 protection. HR departments must lead the conversation that addresses employee trust, corporate responsibilities, and new technology’s ethical implications. Organizations need to tackle the ethics and privacy topic and be open and transparent to build and maintain employee trust in the use of their data.

Business leaders must ensure no logical tension between what is good for the employer and what is good for the employee. But the warning in the article is clear: The temptation to force people into certain behaviors, or to use their data against them, is more real than one would think.

Workforce AI Ethics in a social context

AI ethics groups are repeating one of society’s classic mistakes

Abhishek Gupta and Victoria Heath

The global AI ethics efforts aim to help everyone benefit from this technology and prevent it from harming. International organizations are racing to develop global guidelines for the ethical use of AI. However, these efforts will be futile if they fail to account for the cultural and regional contexts in which AI operates. Without more geographic representation, they will produce a global vision for AI ethics that reflects people’s perspectives in only a few regions of the world, particularly North America and northwestern Europe.

“Fairness,” “privacy,” and “bias” mean different things in different places. People also have different expectations of these concepts depending on their own political, social, and economic realities. If organizations working on global AI ethics fail to acknowledge this, they risk developing standards that are, at best, meaningless and ineffective across all the world’s regions. At worst, these flawed standards will lead to AI tools that preserve existing biases and are insensitive to local cultures.

To prevent such abuses, companies working on ethical guidelines for AI-powered systems need to engage users from around the world. They must also be aware of how their policies apply in different contexts. Unfortunately, the entire field of AI ethics is still at risk of limiting itself to languages, ideas, theories, and challenges from many regions. Nevertheless, the article enumerates some encouraging attempts to change this situation.

Edition #4 – September 2020

Workforce AI Ethics in strategic thinking

The Ethics of AI Ethics: An Evaluation of Guidelines

Thilo Hagendorff

The advanced application of AI in many fields raises discussion on AI ethics. Some ethics guidelines are already published. Although overlapping, they are not identical. So, how can one evaluate ethics guidelines? This article compares 22 approaches. Its analysis provides a detailed overview of AI ethics and examines the implementation of ethical principles in AI systems.

Unfortunately, according to this article, AI ethics is currently failing: Ethics lacks a reinforcement mechanism, and so, deviations from various codes of ethics have no consequences. Integrated Ethics into institutions serves mainly as a marketing strategy. Reading ethics guidelines has no significant influence on software developers’ decision-making, who lack a feeling of accountability or a view of the moral significance of their work. Furthermore, economic incentives are easily overriding commitment to ethical principles and values.

In several areas, ethically motivated efforts are undertaken to improve AI systems, particularly in fields where specific problems can be technically fixed: privacy protection, anti-discrimination, safety, or explainability. However, some significant ethical aspects that I find relevant to Workforce AI are yet omitted from guidelines. These are a lack of diversity in the AI community, the weighting between algorithmic or human decision routines, “hidden” social costs of AI, and the problem of the public–private-partnerships and industry-funded research.

In order to close the gap between ethics and technical discourses, a stronger focus on technical details of AI and ML is required. But at the same time, AI ethics should focus on genuinely social aspects, uncover blind spots in knowledge, and strive for individual self-responsibility.

Workforce AI Ethics in practical advice

Career Planning? Consider These HR Technology Roles of the Future

Dave Zielinski

Artificial intelligence technologies and other automation solutions are disrupting the HR profession. A crucial part of HR response is to consider new responsibilities within their roles. It is not surprising to find this topic in HR-related content. However, it is encouraging to see that this sector feels AI Ethics as a part of its future domain. While general predictions about future roles are not necessarily useful, experts’ discussion about AI Ethics offers practical points that can serve us today.

Although the AI Ethics Officer is mentioned as a future role, its description shed some light on present necessities. As new technologies are adopted by HR and generate unprecedented amounts of data about employees and candidates, the data must be carefully assessed, used, and protected. Furthermore, since decisions to deploy AI and ML are often made in departments other than HR, HR leaders must have a voice in ensuring AI-generated talent data is used ethically, so potential bias is prevented.

What does this mean for HR practitioners in organizations today? First, it is time to establish new practices in collaboration with the legal team to ensure the algorithms’ results are transparent, explainable, and bias-free. Moreover, it is time to start considering the balance between stakeholders in the organization. The HR department should ask how technologies serve both employers and employees and not settle only in discussing what technologies they should be using.

(Thanks for sharing, Vijay Bankar)

Workforce AI Ethics in product reviews

Google Offers to Help Others With the Tricky Ethics of AI

Tom Simonite

This entry is not related solely to Workforce AI. However, since all tech giants are players in the HR-Tech industry this way or another, I find this article thought-provoking. Today organizations receive cloud computing solutions from vendors like Amazon, Microsoft, and Google. Will they outsource the domain of AI Ethics to those vendors too? It turns out that Google’s cloud division will soon invite customers to do so.

Google AI ethics services, which the company plans to launch before the end of the year, will include spotting racial bias in computer vision systems and developing ethical guidelines that govern AI projects. In the long run, it may offer AI auditing for ethical integrity and ethics advice. Will we see a new business category called EaaS, i.e., ethics as a service? And if so, would it be right to consider companies such as Google to suppliers of such services?

On the one hand, Google has learned some AI ethics lessons the hard way, e.g., accidentally labeling black people as gorillas, which is the tip of the iceberg when considering how facial recognition systems are often less accurate for black people. Therefore, Google can leverage its experience and power to promote AI Ethics. But on the other hand, a company seeking to make money from AI may not be the best moral mentor on restraining technology. The inherent conflict of interest is relatively straightforward. Nevertheless, it is worthwhile to stay tuned for Google’s training courses on the topic.

Workforce AI Ethics in a social context

Employers are tracking us. Let’s track them back

Johanna Kinnock

Employee surveillance grows, and most employers are tracking their workers in one way or another. Research firm Gartner says half of the companies were already using “non-traditional” listening techniques like email scraping and workspace tracking in 2018. They estimate the figure to have risen to around 80% by now. Should employees worry? Should they respond to protect themselves? Workplace data expert Christina Colclough thinks they should. Colclough has created an app, WeClock, that enables employees to track their data and share it with unions.

Employees and their unions need to push back to ensure that their whole online existence doesn’t become their employers’ property. Data from employee surveillance is used to boost productivity, gain competitive advantage, and grow profits, but it cements the position of power that employers have over employees. Regulation for individual rights to data does not offer sufficient remedy yet. Decisions about employees and candidates present or take away certain opportunities based on past actions. Algorithms may not show certain job offers or career opportunities. There is a vast gap between what companies know about employees and what employees know about themselves.

Digitization doesn’t necessarily mean that only employers should have control and access over employee data. The app WeClock enables employees to track, and share with their unions, things like how far they must travel to work, whether they’re taking their allotted breaks, and how long they spend working out of hours. This will provide a source of aggregate data about critical issues affecting employee wellbeing.

Edition #3 – August 2020

Workforce AI Ethics in strategic thinking

Questions about your AI Ethics

John Sumser

Do words like bias, privacy, liability, design, and management are raised in strategic discussions in your organization? And if so, are such words followed by an exclamation mark or a question mark? I consider this article as strategic, not merely because it covers 24 ethical questions that you should think about when implementing AI, but because it is actually an infinite list of questions. Each question you raise may bring more questions instead of answers. As AI technology evolves and penetration rates in organizations sharply increase, this list will probably demonstrate some of our routine discussions.

Some questions I find most important are: What are the limits of our intrusion into worker’s behavior and sentiments? What rights do employees have on information about themselves? How do we treat our workers who are not employees (gig workers, temps, subcontractors)? Is our machine-led learning system actually developing our organization in the direction we want? How, exactly, do you tell if the machine is producing the results you actually want and need? But read through the entire list, and add your own.

The ethics of AI is more than a committee that produces hard rules. The implementation is not only technical but rather an obligation to have a clear sense of what the organization’s ethics are. It may bring many new questions. However, in a reality of rapidly evolving technologies, don’t be surprised that a reasonable answer may be ‘I don’t know’. Simply follow it with ‘How do we find out?’

Workforce AI Ethics in practical advice

INSIGHT: Hiring Tests Need Revamp to End Legal Bias

Ron Edwards

Do artificial intelligence push recruitment practices toward less fairness? Pending legislation in New York City and California may suggest it does. Is it a first step in ending legal hiring bias? This call to update legislation in the US, specifically, revamp hiring tests to end legal bias is an eye-opening perspective to all prospects and clients of AI solutions. Although targeted to government institutions, its argument can be considered as advice to everyone in this field. Don’t wait for regulation to critic what vendors put on the shelves.

The article describes how hiring tools can negatively impact women, people of color, and those with disabilities. e.g., analyzing facial expressions using AI software, or collecting information unrelated to a job in question. Employers use cognitive ability assessments that enable significantly more white candidates to pass, in comparison to minorities. A high-profile failure is also mentioned: Amazon built an AI hiring tool that filtered out women’s resumes for engineering positions.

For workforce diversity to improve, 20th-century laws should be updated in accordance with 21st-century technologies. California and New York City are considering legislation that would set standards for AI assessments in hiring. Its requirements include pre-testing for bias, annual auditing to ensure no adverse impact on demographic groups, and candidates’ notification about the characteristics assessed by AI tools — a positive direction that organizations should embrace even before the long processes of legislation end because all candidates deserve equal chance to get hired, promoted, and be rewarded consistent with their talents.  


(Thank you Jouko van Aggelen for sharing)

Workforce AI Ethics in product reviews

Why using technology to spy on home-working employees may be a bad idea

Gabriel Burdin, Simon D. Halliday, and Fabio Landini

I’ve already offered in this section dystopian descriptions of employee surveillance while working from home. Some remote employees are photographed along with their desktop screenshots every few minutes. Others are tracked while browsing the web, make online calls, post on social media, and send private messages. The purpose of such surveillance solutions is to provide employees incentives to maintain their productivity, or in other words, prevent them from slacking off or shirking on working hours. However, psychological experiments reveal that instead of boosting or maintaining productivity, the variety of surveillance solutions might lead to the opposite consequence.

Research findings show that using technology to spy on home-working employees may be a bad idea after all. The standard economic theory would predict that intensive online workplace surveillance is effective since employees are motivated purely by self-interest and care only about their material payoffs. However, empirical evidence suggests that people have more complex motives. Alongside material payoffs, people value autonomy and dislike external control. They are also motivated by reciprocity and their beliefs about others’ intentions. Employees reward trusting employers who avoid control with their own efforts. Employers may trigger employees’ positive reciprocity and support their productivity simply by desist greater control.

Interestingly, the debate about remote workforce surveillance, which I included in previous editions of this monthly review, was focused mainly on employee privacy and the blurred boundaries between work and non-work. These perspectives, as much as important, are not comprehensive enough to understand the employment relations and conflicts. While employers would like to boost productivity for profit, surveillance technologies that monitor work from home might be the wrong solution, because it signals distrust and reduces intrinsic motivation to perform well. Ignoring the potential reactions to surveillance solutions may undermine the goal of increased productivity, let alone harming employees’ dignity.

Workforce AI Ethics in a social context

21 HR Jobs of the Future

Jeanne C. Meister, Robert H. Brown

Some writers perceive the Covid19 times as a tremendous opportunity for the HR sector to lead organizations in navigating the future. But a more realistic perspective would emphasize that in this turbulent time even the best intentions to support the people and guiding them to acquiring new skillset and embracing new career paths won’t help if the business crush due to covid19. In other words, it’s not just the employees who need to overcome, the organizations which employ them need to survive the crisis. However, I do witness a mindset shift in the HR sector, which in my opinion represents a continuous development, that covid19 may accelerate but certainly did not create. For that reason, I was happy to read about research that demonstrated such a shift, and creatively described 21 HR jobs of the future.

Nearly 100 CHROs, CLOs, and VP’s of talent and workforce transformation participated in brainstorming and considered economic, political, demographic, societal, cultural, business, and technology trend to envision how HR’s role might evolve over the next 10 years. The hypothetic future HR roles they created represent a growing understanding of crucial issues such as individual and organizational resilience, organizational trust and safety, creativity and innovation, data literacy, and human-machine partnerships. Those issues and the roles derived are not necessarily in the HR domain. However, the perceptions of HR leaders represent pivoting in the organizational state of mind.

As questions start being raised around the potential for bias, inaccuracy, and lack of transparency in workforce AI solutions, more senior HR leaders understand the need for systematically ensuring fairness, explainability, and accountability. The writers believe this could lead to HR roles such as the Human Bias Officer, responsible for helping mitigate bias across all business functions. I believe it’s an encouraging direction in organizations’ agendas toward responsibility in the broad social context. And so, I’m happy to end this monthly edition with such a positive perspective.

Edition #2 – July 2020

Workforce AI Ethics in strategic thinking

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Samuele Lo Piano

More and more decisions related to the people aspects of the business are being based on machine-learning algorithms. Ethical questions are raised from time to time, e.g., when “black box” algorithms create controversial outcomes. However, until writing these lines, I have not found a single standard or framework that guides the HR-Tech industry beyond regional regulations.

By the time such a standard established, any practitioner who deals with the subject needs a thorough review of literature that leads to available tools and documentation. This Nature’s article offers the solutions. Although it addresses ethical questions related to risk assessments in criminal justice systems and autonomous vehicles, I consider reading it a strategic step towards ethical considerations in the procurement of workforce AI. Particularly, the article focuses on fairness, accuracy, accountability, and transparency, and offers guidelines and references for these issues.

The article lists research questions around the ethical principles in AI, offers guidelines and literature on the dimensions of AI ethics, and discusses actions towards the inclusion of these dimensions in the future of AI ethics. If you start the journey toward understanding the ethics of workforce AI, you should use this article as an intellectual hub for further exploration of academic and practical conversations.

(Thank you Andrew Neff for the tweet)

Workforce AI Ethics in practical advice

23 sources of data bias for #machinelearning and #deeplearning

ajit jaokar

This list includes 23 types of bias in data for machine learning. Actually, it quotes an entire paragraph of this survey results on bias and fairness in ML. Why I put this content in the practical advice section of this monthly review? I think that although most business leaders in organizations may not be legally responsible for such biases in workforce AI, at least not directly, they do need to be aware of them, ethically. After all, AI support decision-making, but the last words are still owned by humans, who must take into account everything, including justice and fairness.

It’s good to have such a list. I advise you to come back to it from time to time, to refresh your memory and be inspired. So, what kind of biases you can find in this list? Plenty: Aggregation Bias, Population Bias, Simpson’s Paradox, Longitudinal Data Fallacy, Sampling Bias, Behavioral Bias, Content Production Bias, Linking Bias, Popularity Bias, Algorithmic Bias, User Interaction Bias, Presentation Bias, Social Bias, Emergent Bias, Self-Selection Bias, Omitted Variable Bias, Cause-Effect Bias, Funding Bias. Did you try to test yourself and count how many of these biases you already know?

Some biases listed here can be resolved by research methodology. That’s the reason I include some examples of such biases in my introductory courses. So if you are a People Analytics practitioner, don’t hesitate to re-open your old notebooks. Here’s one of my favorites, i.e., I enjoy presenting it to students: Simpson’s Paradox! It arose during the gender bias lawsuit in university admissions against UC Berkeley. Sometimes subgroups, and in this case – women, may be quite different. After analyzing graduate school admissions data, it seemed like there was a bias toward women, a smaller fraction of whom were being admitted to graduate programs compared to their male counterparts. However, when exploring admissions data separately and analyzing it across departments, findings reveal that more women actually applied to departments with lower admission rates for both genders.

Workforce AI Ethics in product reviews

Remote working: This company is using fitness trackers and AI to monitor workers’ lockdown stress

Owen Hughes 

PwC was harnessing AI and fitness-tracking wearables to gain a deeper understanding of how the work and external stressors are impacting employees’ state of mind. During the COVID-19 crisis, companies promote healthy working habits to ensure employees are provided with the support they need while working from home. What can a company offer beyond catch-ups on Zoom? PwC approach is novel, yet, to me, controversial.

The company has been running a pilot scheme that combines ML with wearable devices to understand how lifestyle habits and external factors are impacting its staff. Employees volunteered to use fitness trackers that collect biometric data and connect it to cognitive tests, to manage stress better. Factors such as sleep, exercise, and workload influence employee performance, Obviously, and balancing work and home life benefits mental health and wellbeing.

Volunteering rates were higher than expected. Understanding of human performance and human wellness is, clearly, an interest of both employees and employers. However, in my opinion, it must initiate a discussion about the boundaries of organizational monitoring. Is it OK to collect employee biometric measures, e.g., pulse rate and sleeping patterns, and combine them with cognitive tests and deeper personality traits, in the organization arena? If it does, how far is it OK to go with genetic information? How different are these answers in case the employer also offers medical insurance as a benefit to its employees? Tracking mental and physical responses to understanding work may be essential. Still, employers may provide education and tools without being directly involved in data collection and maintenance. Even when volunteered, there always a self-selection bias among employees (see the previous category in this review), and so, the beneficial results are not equally distributed.

(Thank you David Green, for the tweet)

Workforce AI Ethics in a social context

Man is to Programmer as Woman is to Homemaker: Bias in Machine Learning

Emily Maxie

We often hear about gender inequities in the workplace. A lot of factors are at play: the persistence of traditional gender roles, unconscious bias, blatant sexism, lack of role models for girls who aspire to lead in STEM. However, technology is also to blame because machine learning has the potential to reinforce cultural biases. This article is not new, but it offers a clear explanation for the non-techies on how natural language processing programs exhibited gender stereotypes.

To understand the relationships between words, Google researchers created in 2013, a neural network algorithm which enables computers to understand human speech. To train this algorithm, they used the massive data set at their fingertips: Google News articles. The result was widely accepted and incorporated into all sorts of other software, including recommendation engines and job-search systems. However, the algorithm created troubling correlations between words. It was working correctly, but it learned the biases inherent in the text on Google News.

In order to solve the issue, researches had to identify the difference between a legitimate gender difference and a biased gender difference. They set out to determine the terms that are problematic and exclude them while leaving the unbiased terms untouched. Bias in training data can be mitigated, but only if someone recognizes that it’s there and knows how to correct it. Sadly, it would be impossible to tell if all the uses, in all kinds of software, are fixed, even if Google corrected the bias.

(Thank you Max Blumberg for highlighting this article)

Edition #1 – June 2020

Workforce AI Ethics in strategic thinking

Ethics and the future of work

Erica Volini, Jeff Schwartz, Brad Denny

The way work is done changes, as the integration between employees, alternative workforces, technology, and specifically automation, becomes more prevalent. Deloitte’s article Ethics and the future of work enumerate the increasing range of ethical challenges that managers face in result. Based on a survey, it states four factors at the top of ethical challenges related to the future of work: legal and regulatory requirements, rapid adoption of AI in the workplace, changes in workforce composition, and pressure from external stakeholders. Organizations are not ready to manage ethical challenges. Though relatively prepared to handle privacy and control of employee data, executives’ responses indicate that organizations are unprepared for automation and the use of algorithms in the workplace.

According to Deloitte, organizations should change their perspective when approaching new ethical questions, and shift from asking only “could we” to also asking “how should we.” The article demonstrates how to do so. For example, instead of asking “could we use surveillance technology?” organizations may ask “how should we enhance both productivity and employee safety?”.

Organizations can respond to ethical challenges in various ways. Some organizations create executive positions that focus on driving ethical decision-making. Other organizations use new technologies in ways that can have clear benefits for workers themselves. The point is that instead of reacting to ethical dilemmas as they arise, organizations should anticipate, plan for, and manage ethics as part of their strategy and mission, and focus on how these issues may affect different stakeholders.

 

Workforce AI Ethics in practical advice

Walking the tightrope of People Analytics – Balancing value and trust

Lucas Ruijs

The People Analytics domain will eventually transform into AI products. In the early days, most People Analytics practices were projects or internal tools developed in organizations. As the industry matures, more and more organizations automate, starting with their HR reporting. HR-tech products and platforms that offer solutions based on predictive analytics and natural language processing are not rare anymore, although mostly seen in large organizations. However, the discussion about Ethics in HR-tech is still in its infancy. In my opinion, the conversation between the different disciplines – HR and OD, ML and AI, and Ethics – are the building blocks of the People Analytics field in the future. The article Walking the tightrope of People Analytics – Balancing value and trust is an excellent example of such a multidisciplinary conversation.

People Analytics projects might go wrong in many ways. To prevent the harmful consequences of lousy analysis, HR leaders must ask essential questions about the balance of interests between the employer and the employees, the value delivered to each party, the fairness, and transparency of the analysis and the risk of illegal or immoral application of the results. The HR sector needs an ethical framework to address these questions.

This article takes this need a step further. It defines ethics, review its three primary paradigms, i.e., deontology, consequentialism, and virtue ethics. Then it derives practical principles from each method, respectively – transparency, function, alignment. Each of these principles offers three questions that should be raised before, during, and after an analytics project. This framework goes beyond the regulation. It helps to make sure that new analytics capabilities that improve decision making are not sacrificing employee care.

(Thank you David Green, for the tweet)

Workforce AI Ethics in product reviews

This startup is using AI to give workers a “productivity score”

Will Douglas Heavenarchive

In the last few months, the covid19 pandemic caused millions of people to stop going into offices and doing their jobs from home. A controversial consequence of remote work was the emerging use of surveillance software. Many new applications enable employers now to track their employees’ activities. Some record keyboard strokes, mouse movements, websites visited, and users’ screens. Others monitor interactions between employees to identify patterns of collaboration.

The MIT technology review covered a startup that uses AI to give workers a productivity score, which enables managers to identify those who are most worth retaining and those who are not. The review raises an important question: do you owe it to your employer to be as productive as possible, above all else? Productivity was always crucial from the organizational point of view. However, in a time of the pandemic, it has additional perspectives. People must cope with multi challenges, including health, child care, and balancing work at home with personal needs. But organizations struggle too, to survive. The potential conflicts of interest, and the surveillance available now, put additional weight on that question.

When runs in the background all the time, and monitoring whatever data trail a company can provide for its employees, an algorithm can learn typical workflows of different workers. It can analyze triggers, tasks, and processes. Once it has discovered a regular pattern of employee behavior, it can calculate a productivity score, which is agnostic to the employee role, though it works best with repetitive tasks. Though contributing to productivity by identifying what could be made more efficient or automated, such algorithms also might encode hidden bias, and also might make people feel untrusted.

 

Workforce AI Ethics in a social context

The Institute for Ethical AI & Machine Learning

The Institute for Ethical AI & Machine Learning is a UK-based research center that carries out technical research into processes and frameworks that support the responsible development, deployment, and operation of machine learning systems. The institute’s vision is to “minimize the risk of AI and unlock its full power through a framework that ensures the ethical and conscious development of AI projects.” My reading about this organization’s contribution is through a lens of workforce AI applications. However, this organization aims to influence all industries.  

Volunteering domain experts in this institute articulated “The Responsible Machine Learning Principles” that guide technologists. There are eight principles: Human augmentation, Bias evaluation, Explainability by justification, Reproducible operations, Displacement strategy, Practical accuracy, Trust by privacy, and Security risks. Each principle includes a definition, detailed description, examples, and resources. I think every workshop for AI developers should cover these principles, and especially in the HR-Tech industry.

The Institute for Ethical AI & ML offers a valuable tool, called AI-RFX. It is a set of templates that empowers industry practitioners who oversee procurement to raise the bar for AI safety, quality, and performance. Practically, this open-source tool converts the eight principles for responsible ML into a checklist.

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]]> Leveraging workforce data as it was a state security project https://www.littalics.com/leveraging-workforce-data-as-it-was-a-state-security-project/ Wed, 02 Sep 2020 08:34:02 +0000 https://www.littalics.com/?p=3186 An interview about People Analytics with a Lieutenant Colonel in the Israeli Military intelligence - A rare chance to explore practices in the most secure organizations, and to discuss experience with AI, business insights and ethics.

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Imagine the highest degree for sophisticated data usage. If there was such a degree, which organizations would be nominated to hold it? Undoubtedly, the Israeli intelligence corps would be at the top of the list. Could you imagine People Analytics practices in such an organization? Personally, I would love to have a sneak peek into the People Analytics function of this organization. Wouldn’t you?

I was excited to talk with Limor Pinto, a Lieutenant Colonel in the Israeli Military intelligence, who will retire in a few weeks. In her last role, she served as a Head of the Behavioral Sciences Branch in the Intelligence Corps Headquarters. I met her for the first time four years ago, when I talked to the IDF Behavioral Sciences department about People Analytics, and later again in another learning opportunity of the Intelligence Corps. Fast forwarding the years, Limor was generously shared with me some of her experiences. Here’s a rare chance to explore the most secure organization in our country and probably the entire world. How do People Analytics practices look from an insider perspective? It’s a lucky day! Let’s find out.

Untypical career steps

LSH: You are an expert in Organizational Behavior Studies, but you ended up as a Workforce AI Leader. It is not a typical career leap forward. How did your service enable it?

LP: One of the critical challenges that the Israeli intelligence corps face is identifying patterns in individuals’ behaviors and predicting their intentions. We must excel in doing so, in preventing events such as terrorist attacks. We leverage AI to predict enemy plans. However, we have similar predictive needs when we handle our workforce. Just as we can spot on a suicide bomber using AI, we can alert out talent intentions to carry a particular behavior, e.g., leave the organization. As a strategic advisor to the high command, I have recommended leveraging our intelligence experience in workforce challenges and adopting AI to predict workforce behaviors. 

LSH: Experts in Behavioral Science can impact organizations in many ways. Why did you decide to focus on People Analytics?

LP: Indeed, behavioral scientists are engaged in research methodologies, like surveys and focus groups, to understand groups and individuals in organizations. However, such methods lack the predictive ability, namely, to associate attitudes and motives to actual behaviors. We tend to interpret research findings based on our experience, but we may be wrong in our judgments and professional gut feelings. People Analytics, and particularly predictive analytics, can cover us. 

Innovation in HR

LSH: Can you give an example of a wrong judgment that the predictive analytics project contradicted? 

LP: Consider, for example, a typical attitude of commanders towards young women officers. They interpreted attrition related to these women’s work-life balance challenges, who handle both career and young families. However, women who did not participate in career succession interviews at the right time, were entirely practical in managing their careers and initiating their next step elsewhere since no one in the army discussed it. Their commanders referred it to these women challenges to cope with the intense routines of Intelligence units. Interestingly, such a pattern was not characterizing male officers.  

LSH: The Intelligence units’ reputation in technology and analytics is well known. But what is it like to lead innovation in Human Resources, which may be considered less glamorous?

LP: My team partnered with tech units and experts. However, innovative leadership was owned by our behavioral sciences practitioners. Some tech experts thought they should own the project and challenged our leadership. But eventually, we established an advisory board that represented all parties, and we were extremely sensitive, so we managed to make everybody feel that they are the owners. This board had an essential part in funding the project. We also had an additional committee of users, comprised of volunteers who contributed to data munging, hackathons, implementation, and even ethics discussions. 

Challenges and wins

LSH: We’ll certainly get back to ethics later, but first, let’s discuss other challenges. It sounds like a part of your challenge in leading workforce AI wasn’t technical, but rather political. 

LP: It was complicated. It’s not easy to do the fundraising internally, but when you succeed in that, the expectations for quick wins are high, while the implementation takes like forever. We experienced the tension between the tech experts and HR practitioners, who were actually on their reskilling journey. The AI experts considered the joint venture as their own and insisted on managing the conversation with programmers and data scientists. The HR practitioners thought it’s an organizational project or intervention to help individuals and commanders. We end-up in assigning a senior officer who basically handled the conflicts daily and prevented the parties from political dead-ends.

LSH: Tell me more about your approach to finding and prioritizing business questions.

LP: Priorities were determined in command discussions, after extracting business questions from a comprehensive organizational diagnosis. We focused on questions related to talent retention, high command promotion, and workforce reaction to upcoming changes in geographic locations of units and compensation.

Actionable workforce insights

LSH: What data sources you had and how did you leverage the integration of data from different sources?

LP: We retrieved structured data about people’s backgrounds and activities from their long journey in the military serves, e.g., psychometrics, demographics, sociometric, and unstructured data from evaluation processes and interviews. We also purchased relevant data about the Israeli labor market and received data from other Israeli army units, concerning commute times, attitudes among tech talents, and more. When we integrated the data from those different sources, we succeeded in offering insights, and particularly, alternative explanations. 

LSH: How did you transform your findings into actionable insights?

LP: We gained a new understanding of daily phenomena and realized that some of our former responses were completely irrelevant. For instance, in the case study of women officer attrition that I mentioned earlier, commanders were required to discuss career paths with their officers right after signing their first contract with the army. We also re-generated academic programs and compensation plans modularly to offer more tailor-made career solutions. Our impact was significant, and eventually, we won the Commander-in-Chief Award for creative thinking. 

Ethics questions

LSH: Let’s go back to the Ethics questions. What kinds of issues were raised, and how did you handle them?

LP: Yes, we had plenty of ethical issues, e.g., notifying individuals about using their data, limiting our sources of data in the appropriate way for the workforce, as opposed to the enemy, limiting permissions to access the data, and more. The senior board handled most of the discussions and decisions. However, we consulted layers, content specialists in the Intelligence Community, and academic researchers in AI ethics.

LSH: In a glance into the future, how this project will mark your career path?

LP: undoubtedly, this project was essentially a start-up within an organization, or should I say, the most institutionalized organization in the State of Israel. It was an opportunity to explore and express myself as an entrepreneur and innovation leader. But most of all, we managed to solve complex problems in the intelligence corps, which we tried to solve for years by our HR strategy. The breakthrough emerged when we transformed HR strategy into an HR data strategy. Predicting workforce behaviors become even more crucial nowadays in Covid19 times. I’m confident that this career-shaping experience will provide value to civil organizations in the public and private sectors in my next career journey as a citizen. 

LSH: Thank you, Limor, for sharing your fascinating experience.

I look forward to following your journey as a citizen expert in People Analytics, and to continue collaborating in educating the next generation of People Analytics leaders in Israel and globally!

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]]> Ethics in People Analytics – The Journey Continues https://www.littalics.com/ethics-in-people-analytics-the-journey-continues/ Wed, 17 Jun 2020 12:43:00 +0000 https://www.littalics.com/?p=2679 I want to help organizations evaluate AI concerning Ethics, or metaphorically, to assist them in knowing how to interview AI, just as they know how to interview their candidates and employees. I'm creating a comprehensive resource list that will be updated monthly.

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My journey in the domain of Ethics in People Analytics started three years ago. Till then, my main interest focused on the ethical conduction of employee surveys and reviews. However, AI changed everything.

The journey began

As I wrote back in 2017, “People Analytics leaders won’t be in charge of the programming, but rather of the procurement in HR-tech and analytics solutions. They will learn, for the sake of regulations and ethics, to ask vendors hard questions and be more critique about model accuracy and data privacy.”

Indeed, ethics is mentioned a lot in the context of People Analytics. However, ethics guidelines and practices in the procurement of workforce AI are still less common. Though I still hold those believes I shared three years ago. People Analytics leaders “will contribute not only to a culture of a data-driven organization but also to a safe work environment regarding employee data.”

Moreover, the change in attitudes towards AI will not pass on employees and candidates. People “will judge employers, in addition to Employee Experience perceptions, by employer ethics in data management, and when feeling secure, they’ll be more receptive and enthusiastic to participate and cooperate with AI and ML to influence their career path.”

Unfortunately, most employees and candidates still lag in understanding the consequences of the increased use of their data. Furthermore, I think that organizations, and in particular, learning functions within HR departments, still have a lot to do to educate the workforce to be informed participants in the future of work.

The discussion expands

In my lectures about Procurement and Ethics in workforce AI, that I ‘ve been offering since 2018, I point to the change in People Analytics roles: “a responsibility for data ethics, i.e., to know what is good or bad and practice this role with moral obligation.” There is a lot that we can do with the data. However, it might not be what we should do.” The compliance with the GDPR and other regulatory issues were only a starting point. It inevitably forced awareness of People Analysts to privacy issues. But I think it should also influence employees’ behavior.

Eventually, the People Analytics domain will have to respond. And so, I wrote: “When people start exercising their rights and request access to their data, People Analytics leaders will be ready in advance to give them comprehensive information about their data usage. When employees start asking to correct or erase their data, employers will request more transparency and security from HR software providers. Organizations will ensure that they process only the personal data that is necessary for the specific purpose they wish to accomplish. Therefore, they’ll need long-term planning and more serious considerations.”

However, that kind of behavior is still rarely observed within the workforce. Nevertheless, I decided to expand the discussion about Ethics in the introductory course I offered to HR departments, called The People Analytics Journey. The fourth module of the course was dedicated entirely to practices of procurement and ethics in People Analytics.

We are not there yet

My takeaway from the experience I had in education HR leaders was that their knowledge gap was too broad. I’m an applied researcher with practical ML background, so obviously, I understand the context and terms of AI. However, the typical HR brain (and most managers’ brains, to be fair) is wired by descriptive or inferential statistics that we all learned sometime in the past. Machine learning is entirely different, and to understand it to the level of dealing with potential ethics risks, let alone algorithm auditing, a basic review is insufficient. Yes, I wrote some guides, and tried to offer explanations to themes that I think everyone should understand, e.g., What AI is – or isn’t? How accurate is AI? Why AI prone to bias? How people react to AI? And how legal frameworks deal with AI?. However, none of them offers a systematic approach and a practical methodology to deal with this evolving field.

And so, I decided to continue the journey with a search, and hopefully, an articulation of such a solution. I want to help organizations to evaluate AI concerning Ethics, or metaphorically, to assist them in knowing how to interview AI, just as they know how to interview their candidates and employees. To do so, I hope to continue my learning and collaboration with colleagues and clients and then share with my readers every step we make. I will create the following comprehensive resource list that will be updated monthly.

“The List” – monthly updated resources

For now, I decided to include four categories in my resource list: Ethics in workforce strategic thinking, Ethics in workforce AI practices, Ethics in product reviews, and Ethics in a social context. I hope that such categorization will facilitate learning in the field. Particularly, leaders need to understand how to incorporate questions about values in their businesses, starting in their strategic planning. Then, they may need a helping hand to translate those values and plans into daily practices and procedures. Those practices can be demonstrated in discussions and reviews about specific products. But at the end of the day, business leaders influence the employees, their families, their communities, and society. Therefore, this resource list must include a social perspective too.

There is an enormous amount of content about “Ethics in People Analytics” online, to judge by Google search results (126 million, and counting). Nevertheless, my list will be exclusive. I will include in it the resources that I found helpful in the progress of creating a systematic approach to evaluate workforce AI ethically. The first edition of “The List” will be published at the end of June. My newsletter subscribers will receive the updated list straight into their mailbox.

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]]> AI for HR – Five themes that you must understand (Part 2) https://www.littalics.com/ai-for-hr-five-themes-that-you-must-understand-part-2/ Tue, 09 Jun 2020 12:15:55 +0000 https://www.littalics.com/?p=2648 In part 1 of this article, I called HR leaders to start the journey to AI by understanding five themes: What AI is - or isn't? How accurate is AI? Why AI prone to bias? How should people react to AI? How legal frameworks deal with AI? In this part of the article, I discuss the last two themes.

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The last module of “The People Analytics Journey” – the introductory course for HR professionals that I taught in Tel Aviv – was dedicated to the future of People Analytics. We discuss the question – Will People Analysts always be human?, and I offered some practice guideline in Procurement and Ethics. As HR practitioners still lag in their understanding of analytics and AI, I think that this module illustrated the path needed to close the gap, without the math and the coding, of course.

In part 1 of the article AI for HR – Five themes that you must understand, I emphasized that the realm of work changes, as every stage of the employee lifecycle is affected by AI. To face the difficulties that we encounter, I called HR leaders to start by understanding five themes: 1) What AI is – or isn’t? 2) How accurate is AI? 3) Why AI prone to bias? 4) How should people react to AI? 5) How legal frameworks deal with AI? In this part of the article, I discuss the last two themes.

How should people react to AI?

There is no magic in AI. However, people who don’t understand algorithms and models may attribute magic to it, simply because they have no other explanation. But how can one discuss the fairness of a misperceived magical phenomenon? How can we, as a society, claim for fairness and justice in the implemented AI solutions, and especially in the workplace, when we don’t understand how AI is different from everything else we know? 

In the previous part of this article, I discussed the accuracy of a Machine Learning model. Unfortunately, people may assume that if a model is accurate (and remember that there is no such thing as 100% accuracy in ML), it is also good enough in terms of fairness. But the accuracy is only one measure to evaluate a model. Unfortunately, accurate models might lead to unfair decision making in organizations, due to bias, or due to an imperfect human decision making that follows.

The challenge gets even larger and more complicated because it is not clear who is the subject of our fairness attributions. Is it the entire organization? The person who represents an organization? The organizational functionality that AI holds? And if they perceive unfairness related to one of these entities, what should people do?

People don’t necessarily know their rights or how to fight for them. They also don’t necessarily know what data their employer uses and how. When presented with a technical solution at work, people may assume that it’s accurate, but they don’t necessarily know how it might weaken their condition. New learning paths and education are needed, and people should start expecting that from their employers.

New learning paths and education are critical because fear sometimes emerges when AI apps replace some roles of humans at work. In future hybrid teams, where humans and AI will work side by side, people might not be able to keep up with the machines. Specifically, they won’t be able to know what exact data algorithms are using and how the data is processed. The complexity is too much for a human to handle, and that may also contribute to fear.

Therefore, HR professionals who perceive their role as handling relationships between people and organizations should start exploring the domain of AI from that angle too. They should consider offering new learning paths that address responsibility for fairness in AI usage. As I stated before when I previously discussed the new roles of HR leaders in the fourth industrial revolution, the discussion about employee experience is pointless without exercising data transparency and fairness. Hopefully, someday, organizations will be rated based on this dimension too.

Useful sources for understanding issues in AI-based decision making are volunteering organizations that effort to prevent injustice, like the Algorithmic Justice League, and the Algorithm Watch, that study the effects of discrimination in AI and publish AI misuse incidents. I hope that the HR sector, and especially its education institutions, will start a collaboration with such organizations, or establish its own.

How legal frameworks deal with AI?

Two years ago, the legal environment changed, with the arrival of the GDPR – the European Union’s General Data Protection Regulation. Recently, the CCPA – California Consumer Privacy Act also went into effect. Other countries and states will adopt similar regulations sooner or later. A lot has been written about data privacy, following Article 9 of the GDPR. Less discussed, at least in HR circles, as much as I know, is Article 22 of the GDPR, which states the right not to be subject to a decision based solely on automated processing, and the right to obtain human intervention and explanation.

Unfortunately, only some kinds of algorithms are easy to explain, e.g., models based on Logistic Regression. More complex algorithms, like Deep Learning, are impossible to interpret. So one may wonder how organizations might violate the law simply because of using algorithms that are hard to explain.

The challenge is not limited to the complexity of algorithms. Sometimes AI developers rely on packages or libraries, i.e., code added to a program that someone else developed, instead of writing the entire program from scratch. But what if those libraries are biased but not checked for bias? What if minorities’ data is ignored in those libraries? It is not clear in such cases who is in charge and who needs to fix it.

I’m not a jurist, and obviously, none of the opinions I share is legal advice. However, since we are in an era when the laws that govern AI are still developing, I think that those who are responsible for the implementation of AI solutions in the organization should follow development not only on the technological side but also on the legal aspects of AI. That’s a lot of burden on HR professionals.

Nevertheless, to maintain the relations between employers and employees, the HR sector should not skip this topic. They must be aware of restrictions and regulations, keep close contact with legal departments, and other professionals, such as CIOs and data security teams. Furthermore, they should keep exploring how other organizations implemented AI successfully and concerning the legal aspect too.

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]]> My opinions about the Ethics of People Analytics and AI https://www.littalics.com/my-opinions-about-the-ethics-of-people-analytics-and-artificial-intelligence/ Wed, 29 Jan 2020 10:50:18 +0000 http://www.littalshemerhaim.com/?p=1946 For a retrospective review, and hopefully, for our continuous conversation, here’s a collection of my opinions about Ethics, People Analytics, and Artificial Intelligence.

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Since 2016, I’ve been sharing in this blog many kinds of content that aimed to push the People Analytics profession forwards. I published interviews with colleagues and clients; I covered conferences and events; I updated my famous list of books and explores tech solutions; But most of all, this blog is my channel to express my opinions, which sometimes are a little ahead of their time. For a retrospective review, and hopefully, for our continuous conversation, here’s a collection of my opinions about Ethics, People Analytics, and Artificial Intelligence. Stay tuned! So much more to come in 2021! (Updated on January 20th, 2021, Total resources: 5)

AI for HR – Five themes that you must understand (2)

(June 2020) In part 1 of this article, I called HR leaders to start the journey to AI by understanding five themes: What AI is – or isn’t? How accurate is AI? Why AI prone to bias? How should people react to AI? How legal frameworks deal with AI? In this part of the article, I discuss the last two themes. To maintain the relations between employers and employees, the HR sector should be aware of restrictions and regulations, keep close contact with legal departments, and other professionals, such as CIOs and data security teams, and keep exploring how other organizations implemented AI successfully and concerning the legal aspect too. Read More

AI for HR – Five themes that you must understand (1)

(January 2020) Implementing Artificial Intelligence (AI) in the workplace offers interesting opportunities to increase results and impact for various stakeholders. However, it also introduces ethical challenges. I find HR practitioners still lagging in their understanding of this domain, though their role in this field, as I see it, is crucial. Therefore, I dedicated a significant portion of my talks and training programs in the last year to close this gap (without Math and Coding, so don’t worry!). In particular, I discussed concepts and topics that, on my opinion, enable a better consideration of AI solutions in the workplace in a more informed way. Read More

New Roles of HR Leader in The 4th Industrial Revolution

(June 2019) HR departments practice People Analytics to help business leaders to improve performance and growth through insights from people’s data. But what’s beyond People Analytics? How HR leaders should be prepared for the fourth industrial revolution? 1) AI changes everything. We have new responsibilities. 2) New learning path. New employer rating. 3) New skills. HR people are not there yet. Read More

Will People Analysts always be human?

(May 2018, based on my Lecture at the HR & People Analytics Forum Budapest. See list of Public Speaking) We heard the words that every speaker emphasized in this conference: measures, KPIs, metrics, models, predictions, insights. And of course, People Analytics. These are important words. They are all related to our practices today. We have to measure, keep track of our KPIs, use advanced analytics to get business insights. We all do or intend to do, People Analytics. But will our practices last, facing the rapid change in technology? How will our jobs as People Analysts will change in the future? Will People Analytics remain a job for humans? And if it will, what will we – humans do, when machines can do analytics much better than us? Read More

Employee in the big data era: Will you let robots determine your future at work?

(October 2017, A version of this article was published in TLNT magazine) Think about data that you share at work, in the most personal sense. You share with your employer, and sometimes with potential employers, so many aspects of your life: details about your professional path, your personal status, health care, social-economics, legal and geographical background. You also agree to share information about what you do in different times and places, who you meet, what information you consume, and so on. Moreover, you leave your digital footprints on the web, social networks, and different apps, where data reveals to employers a lot about you. Did you ever consider how data might affect you at work? How does your employer actually use the data about you, and how technology enables it? What is allowed to do with your data, and what is considered crossing a red line, in terms of ethics and regulations? Read More1

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]]> AI for HR – Five themes that you must understand (Part 1) https://www.littalics.com/ai-for-hr-five-themes-that-you-must-understand-part-1/ Wed, 29 Jan 2020 10:07:23 +0000 http://www.littalshemerhaim.com/?p=1939 To face both technical and social difficulties related to AI, every HR leader should start understanding 5 themes: What AI is – or isn’t? How accurate is AI? Why AI prone to bias? How people react to AI? How legal frameworks deal with AI? This part discusses the first 3 themes.

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(First published: January 29th, 2020. Updated: July 26th, 2023)

Implementing Artificial Intelligence (AI) in the workplace offers interesting opportunities to increase results and impact for various stakeholders. However, it also introduces ethical challenges. I find HR practitioners still lagging in their understanding of this domain, though their role in this field, as I see it, is crucial. Therefore, I dedicated a significant portion of my talks and training programs in recent years to close this gap (without Math and Coding, so don’t worry!). In particular, I discussed concepts and topics that, in my opinion, enable a better consideration of AI solutions in the workplace in a more informed way. 

I don’t think anyone can describe in a single lecture the entire ways that AI will affect the realm of work and organizations. But I do believe that every stage of the employee lifecycle will be positively affected by AI. And we will also encounter challenges, both technical and social. To face those difficulties, I call every HR leader to start by understanding these five themes: 1) What AI is – or isn’t? 2) How accurate is AI? 3) Why is AI prone to bias? 4) How do people react to AI? 5) How do legal frameworks deal with AI? In this part, I discuss the first three themes. In part 2 of this article, I continue the conversation with the remaining themes.

What AI is – or isn’t?

If I ask you to close your eyes and imagine Artificial Intelligence (really, try that for a second), you’ll probably come up with a robot representation. Maybe you’ll imagine a smart machine that interacts with the environment and learn from its experience. Maybe your imagination will lead you to think about a futuristic robot-buddy, that can offer help without your specific instructions, and that can use logical reasoning and visual perception to be a substitute for a human interlocutor. Indeed, these are all nice associations. However, they are far from what AI is, in the context of HR-tech solutions that are offered today.

Most HR-tech solutions are applications related to the field of Machine Learning (ML). ML refers to a collection of algorithms that learn from specific datasets and then classify or predict outcomes. So, as our imagination navigates to ideas about AI as thinking machines that can reason and learn anything, like a human may do, only better, AI essentially focuses on a specific domain, and offer a specific prediction. In the context of work, these predictions may be regarding fitting into a role or team, performance at work, and employee attrition. Since AI helps to reduce the complexity of a business question related to people, it has a growing portion in the HR-tech markets and venture capital.

How accurate is AI?

The easiest way to understand ML is to think about it as a classification challenge. AI automates classification in two major approaches: Supervised Learning, in which AI assigns new observations with existing categories based on matches with the previous examination of data; and Unsupervised Learning, in which AI groups cases based on similarity without specific criteria. But in both approaches, when you automate classification, things can go wrong, and eventually mislead decision-making. And so, the importance of accuracy arises.

How can classification errors in AI be measured? Well, I promised no Math, but at this point, I must mention that predictions or outcomes which an algorithm produces, do have numeric measures of accuracy. The important point here is that these accuracy measures are always less than perfect. In a previous article about predicting employee attrition, I demonstrate in detail some measures of accuracy. But for our discussion now, let’s focus on two terms: Sensitivity and Specificity. Sensitivity relates to the “true positive” rate, i.e., the number of cases that should be classified in a category. Think about employees who fit in a role – we want to make sure that those cases are identified correctly as much as possible. Specificity relates to the “true negatives” rate, i.e., the cases that shouldn’t be classified in that category. Sensitivity and Specificity are independent measures that are assessed separately, but when combining the information from the two of them, some other accuracy measures are created.

Obviously, as a client or buyer of AI applications, you are not the one who should take those measures. But it is your job to be aware of accuracy issues and to expect the vendors who produce algorithms to specify the accuracy for you, just like as you would expect a medicine supplier to report about possible side effects. It is also your job to consider accuracy issues when you base your decision-making about AI algorithms, especially when people’s lives are at stake. When AI mispredicts your favorite song on Spotify, the consequences are not truly harmful. But it is certainly not the case when you are based on mispredictions at work.

Why is AI prone to bias?

There are a lot of discussions about AI that reduces human biases, and parallelly, many discussions about AI that reflects human biases. Both claims are true since bias depends on the data that we use to create algorithms and the way that we use those algorithms. So, how bias occurs? Let’s explore some examples.

ML starts with datasets, and bias might start in datasets too. If a dataset is limited in terms of variability i.e., it does not cover some possible classes of cases, the resulting classification may be biased. The algorithm’s accuracy may be well evaluated regarding general results for the entire population, but it can still be biased for small subgroups. To reduce bias, it is crucial to make sure that the training dataset that is used for building the algorithm is diverse in accordance with both common and scarce cases. For instance, think about the occupational recommendations that are related to minorities. If a training dataset does not include records for minorities, predictions and recommendations about those minorities may be biased. This already happened in reality, at Amazon’s recruitment automation, which was found to be gender discriminant. Furthermore, bias may occur during the feature engineering stage, when you select which variables are included in a model. Feature engineering significantly influences a model’s prediction accuracy. However, while its impact on accuracy is easy to measure, its impact on the model’s bias is not. 

Another source of bias may be the subjective evaluations that are included in datasets. I recall my experience two decades ago when I participated as a rater in huge psychiatric research. The preparations for the fieldwork included raters training in order to create calibration within the team. The head of the research, a professor of Psychiatry and the head of the Psychiatry department in one of the largest hospitals in my country, wanted to make sure that every mental symptom that we encounter would be classified the same by every rater in the team. For that reason, he decided to train the team by himself. Interrater reliability is also relevant to AI that is based on the labeling that humans produce. In those use cases, subjective judgments must be calibrated, so the labeled data would not be affected by perceptions and cognitive human biases.  

It is also worth mentioning that algorithms and humans are different in the way they react to new and surprising information. Humans are prone to anchoring bias, and their judgments are sometimes so prominent that they tend to ignore or resist contradicting new information. However, when something dramatic is happening, a person may completely change his impression in seconds. Algorithms can’t do that. They will continue to be consistent with classification or prediction for quite some time, even when the new data is essentially shifted. They will catch-up over time, but they can’t change their model at once.

Bias also occurs when we simply use predictions. The idea of a self-fulfilling prophecy is relevant to the discussion about bias in AI because sometimes, an employee or a manager can create the reality that the algorithm is supposed to predict. Think, for example, about an employee who is considered to have a high flight risk according to some algorithm. The prediction of his high probability of leaving the company might influence his manager’s behavior, which in turn will actually push the employee out, even if the prediction was an error in the first place.

These examples are only the tip of the iceberg. I believe that in the near future, People Analytics leaders who will be in charge of the Procurement and Ethics of AI applications in an organization will have to go much deeper into understanding sources of bias. In part 2 of this series, I’ll discuss how people react to AI and how legal frameworks deal with AI.

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]]> The role of technology in the evolution of People Analytics https://www.littalics.com/the-role-of-technology-in-the-evolution-of-people-analytics/ Sat, 30 Nov 2019 20:59:38 +0000 http://www.littalshemerhaim.com/?p=1869 An interview with a former HR analyst at Microsoft, discussing the role of technology in People Analytics and data Ethics: challenges, success stories, and advice - one of many perspectives we had in "The People Analytics Journey" course.

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]]> (Reading Time: 5 minutes)Another cycle of the introductory course, The People Analytics Journey, is about to end. This training program is unique because it covers the fundamentals of the domain and demonstrates them with real career stories and experiences of HR and People Analytics leaders. Thus, the course contributes to a new professional community in Isreal. In previous sessions, we hosted Gal Moses, People Analytics Lead at Amdocs, who shared her onboarding experience and shed light on some challenges and opportunities. We were also honored to have Michal Shoval, HR manager at GIA, who shared her case study. The last session of the course will be a special one. We’ll discuss the future of People Analytics as a profession, and the importance of new skills, e.g., procurement processes and ethical considerations. Our guest will be Yael Epstein, former HR analyst at Microsoft, who will talk about the role of technology in People Analytics, base on her experience. Here is the interview I had with Yael before the learning session.

 

Background

LSH: Thanks for joining us, Yael. Tell us a little about yourself, your background, your role in PA.

YE: The 1st phase of my career was in health organizations. I have a Masters in Public Health (MPH) from the Hebrew University. My role as the coordinator of the non-clinical quality improvement program in a large tertiary hospital included many aspects of HR including change management, training, and recruiting. This led me to further my education in HR and to explore opportunities in the HR profession. In the 2nd phase, I worked for a few years in a placement agency, and then, in the past 11 years, I worked at Microsoft. I started as a staffing specialist in the R&D center in Israel. 5 years ago, I moved to an HR analyst role in a new global team within the HR function (HRBI).

LSH: The People Analytics function at Microsoft is considered to be one of the leaders in the field. What can you share regarding the vision, mission, and principles?

YE: The vision is, in short, #DataDrivenHR. The mission is to enable Microsoft to make evidence-based decisions about workforce and culture. The principles of driving this mission include delivering insightful research and analytics, providing robust and consistent reporting tools in partnership with engineering teams, delivering timely and accurate measurement of companywide business and HR priorities, ensuring data quality, and upholding employee data privacy and security.

The role of technology

LSH: From your experience, what was the role of technology in the evolution of the People Analytics?

YE: Technology supports all aspects of people analytics. There are many examples: It promotes data security and privacy by ensuring the data is used only by authorized people. It enables the use of data by all HR professionals by an accessible format that is easier to understand and communicate even if you are not an analyst. Self-service data solutions, i.e., Microsoft PowerBI saves time and enable us to focus on deeper analysis rather than providing customized data needs. It also enables us to integrate data from various sources and create powerful data models, using visualizations during the analysis and for communicating insights and recommendations. We also can leverage data that wasn’t accessible before, by Workplace Analytics, a company product that enables us to identify collaboration patterns that impact productivity, workforce effectiveness, and employee engagement, based on data from Office 365. We also use text analytics to leverage a huge amount of data from responses to open-ended questions in employee surveys, objectively, and in several languages. More useful products of Microsoft are Yammer, which is an enterprise social networking service that helps us to communicate learnings and ideas, and Teams, which helps to manage resources.

LSH: Tell us more about your role: who were your clients, how did you support them?

YE: People analytics is an evolving field, and I was fortunate to partner with my colleagues in the HRBI team and with HR leaders and managers across the globe. Over time, I partnered with HR teams in both engineering groups and the sales organization. We leveraged an analytical approach to enable the business and HR to execute data-driven decisions in many aspects of the employment cycle, including hiring, headcount trends, diversity, retention, rewards, compensation, as well as candidate and employee sentiment. We partnered with HR to support ongoing HR processes as well as answering specific questions and hypothesis which were raised from their work with the business. One of the best practices was to set milestones during the analysis process in which we shared the work we did thus far, got the perspective and thoughts from our partners before we continued. This ensured that the deliverable answered the needs. Another major aspect of our work was promoting a data-driven approach in HR through one-on-one consultation, standard training on a data-driven approach, and tailored training on specific subjects.

LSH: What do you consider as challenges in your role with respect to technology?

YE: While it is a positive challenge, the ongoing development of knowledge in this field in general and of technology, in particular, requires ongoing learning. Ensuring that you take the time to learn with a very busy day to day work is challenging but also essential and very rewarding. Some other challenges are connected to the fact that technology helps with having more data available for use, thus increasing the need to make sure that we are using the data in an accurate way and to avoid bias throughout the analysis process. The availability of more data also raises the challenge of prioritizing work. One aspect of this is balancing between doing interesting analysis versus doing important analysis. Another aspect is balancing between the sense of urgency that is always driven by the business and the time it takes to do a thorough analysis.

LSH: What do you consider as a success story with respect to technology?

YE: These a great feeling of accomplishment when your HR partners share how they leveraged the tools and knowledge to promote a data-driven approach and seeing our work impacting business decisions. And of course, every time you succeed in a tough technical challenge with your data model or an effective visual you feel a great success. Personally, some of the meaningful cases of success were the use of technology, e.g., text analytics, to help in promoting general values of the unbiased approach, inclusiveness, and collaboration.

Data Ethics 

LSH: How do the people analytics team handle data ethics? Are there processes, partners in the organization, or outside?

YE: Microsoft has a volume of Privacy Standards dedicated to employee data, Data Use Framework for employee data, and Data Protection Notice for employees. Other internal tools and projects have more detailed communications. The company has created an Employee Data Governance Board to provide consistent company-wide direction and oversight on the legal and corporate policy issues reflected in the company’s privacy standards for processing employee personal data. This board is made up of a core team of privacy managers and attorneys for HR, Finance, and IT. Due to the need to combine employee data with business data, a data analytics governance framework was created and is used when embarking on a new people analytics project, to ensure that the right people are involved from the beginning, including legal, HR and any business stakeholders in addition to the people analytics team. We have a privacy manager on the team, who focuses on people analytics data privacy and security, and partners with other roles outside of HR on their use of employee data. We also have mandatory annual training for HR on privacy and data use, which is updated on a yearly basis. Training is helpful in framing the definitions and aspects of ethics, e.g., ensuring a purpose behind each data element and anonymization in reports.

LSH: What would you advise your colleagues whose employers are in an early stage in the field?

YE: Choose as your 1st project subjects that are both important for the business and easy to succeed. Data is available regarding many aspects of HR. Use it to help with important business questions and with building the trust of the business in the data-driven approach. As long as you are aware of the limitations of the data you have, don’t be afraid of doing analysis with partial data. If we wait until we have the “perfect dataset” we will never start doing analysis. Also, showing the value from a partial analysis while being transparent regarding the limitations is a great argument for investing in more resources. As in any other aspect of HR, People Analytics requires ongoing continuous learning. Make sure you leverage resources and collaboration opportunities to continue learning.

LSH: Thank you, Yael! We are fortunate to have your perspective in our course and professional community!

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]]> Actionable insights for the right people at the right time https://www.littalics.com/actionable-insights-to-the-right-people-at-the-right-time/ Wed, 18 Sep 2019 05:58:10 +0000 http://www.littalshemerhaim.com/?p=1730 Professional experts are people you would always want to learn from and be inspired by them. I have the honor to host a colleague who fits this definition, and I'm happy to refer to his open-source contribution.

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How would you define a professional expert in the field of data-driven HR? Certainly, there are many definitions of the People Analytics domain, that may include skills, practices, and responsibilities. However, today for a change, I’d like to suggest a different angle: a professional expert is someone you would always want to learn from and be inspired by. I have the honor to host my colleague from Amsterdam, which definitely fits this definition: Hendrik Feddersen, an expert in HR business processes and analytics, who offers “actionable insights to the right people at the right time”, in a European public sector organization. I didn’t spare Feddersen some hard questions in this interview, and I’m grateful for his thought-provoking answers and his contribution to the diverse opinions in our community.

The path to expertise

LSH: Tell us about yourself, Hendrik, and your background as a People Analytics professional?

HF: Thanks for the interview. I am a senior HR professional, speaker, and the author of HR Analytics Live. I graduated from the Bocconi University in Milan with a degree in Business Administration in 1988 and enjoyed specializing in HR Management, for many years. Having a well-appreciated business acumen, I continuously receive new projects in my current role, to optimize HR processes and HR Information Systems. I’m the Head of Human Resources Information Systems in The European Medicines Agency, which is responsible for the protection and promotion of the public and health, through the evaluation and supervision of medicines.

LSH: There are many paths one can go to reach a People Analytics role. What are the advantages of a path which stems from HRIS offers, and what are the challenges?

HF: At my workplace, I have privileged access to an enormous amount of confidential HR data. My skills in extracting data from SAP and the various SuccessFactors Talent Management modules enable that. Fortunately, I have a strength in nudging action-based on my observations and data extraction. Innovation in HR digitalization comes only on the condition that one understands the detailed HR processes and how those are related to HR data. My challenge, however, is to be able to serve all my internal customers promptly and surpassing their expectations. Yet, excellent customer service depends a lot on up-to-date IT tools.

LSH: Besides your academic background, you are most experienced in on-line learning. To advance one’s skills, would you recommend People Analytics domain-specific programs or general data science programs, and why?

HF: Online learning comes from my insatiable desire for self-development. I learned many of the things that I do now at work, in the last five years. It is an interesting question. To advance one’s skills, I would recommend general data science programs. In the open world, I mean, outside the HR domain, there is so much more that is going on, and people are much more open to sharing their bright ideas. People also receive the credit they deserve. My recommendation is to learn in the open world, and then use the new skills in the HR domain.

The professional community

LSH: Do you think that People Analytics as a profession has an open-source culture? Does openness make a difference in this domain?

HF: I believe that the People Analytics profession doesn’t have an open-source culture. That is a problem, of course, because this way, the People Analytics domain does not develop as fast as data science in general. One of the reasons for this is the HR data, which is, by its nature, sensitive, confidential, and change quickly according to the circumstances. Nevertheless, the People Analytics domain does progress, thanks to proprietary software, and thanks to meeting like-minded professionals at conferences.

LSH: Recently, you published a comprehensive open book about People Analytics practices in R. Tell us about your experience in R. How R is better? Are there barriers to start using it?

HF: Unfortunately learning R demands a steep learning curve. I published all my R code examples applicable to HR. There is no point in keeping them for myself. Any comments for improvement are welcome. I currently use R, mostly for quick and compact operations equivalent to Excel macros. R is much easier to read, and it manipulates data ultrafast. The beauty of R is that it can handle vast amounts of data quickly. There are numerous open-source packages to do all sorts of things. For R, there is a community, while there isn’t one for SAP HCM and SuccessFactors or not one that I am aware of. An open-source programming language is much more fun, and acquired skills are transferable to other companies.

 

A senior’s perspectives

LSH: You have a perspective of two decades in a very special organization: The European Medicine Agency. How did data-driven HR change during these years in it?

HF: That’s right; in fact, I have been doing the same HR activities for the last twenty years more or less. However, the sophistication I have been experiencing is impressive, and it never stops. The arrival of SAP HCM and SuccessFactors Talent Management modules were a breakthrough in producing vast amounts of new HR data. Of course, with more data come more responsibilities and more hick-ups.

LSH: How did The European Medicine Agency, which is a data-driven organization by its nature, contribute to the development of People Analytics? What opportunity it offered, in terms of culture, talent, tools, and investments?

HF: At the European Medicine Agency (EMA), colleagues are brilliant and highly educated. At EMA data protection is a strength. We started implementing data protection already in 2001. The new GDPR for European Union institutions and agencies has given us further impulse. It provides me with a lot of work: drafting of records forms, compliance and risk assessment forms, privacy statements, and description of processes. The very robust selection procedures are another strength at EMA. We are very objective and transparent in our methods. IT tools are undoubtedly necessary to generate HR data and analyze it afterward. It is also true the other way round: to steer an organization with advanced HR Tech tools, you must use the HR data available. Events take place at a breakneck pace. For example, going paperless meant that several colleagues had to get quickly familiar with HR numbers, digital signatures, and reporting tools.

LSH: We usually use, as People Analytics professionals, the mantra of “impacting the business.” What meaning such a mantra has in the public sector?

HF: The European Medicines Agency (EMA) is, indeed, a part of the public sector. However, since it provides services to patients and the pharmaceutical industry, it is unique. For example, EMA is committed to enabling timely patient access to new medicines. EMA promotes innovation and development of new drugs by European small and medium-sized enterprises. The mantra for us could be creating a supportive and fair work environment for the different generations and nationalities, notwithstanding the pressure to do always better and faster. For me, success is when others in the organization follow my line of thoughts and take actions based on the HR data I am providing.

LSH: What would be your advice to HR professionals who want to be more data-driven? From your experience, what is the right way to start?

HF: I wrote about it recently. HR managers are accustomed to making intuitive decisions based on personal experience or judgment. However, it takes time and patience to identify the correct HR data to base managerial decisions. In my view, it is crucial to gain trust from employees. Data quality is a significant challenge too. Not all HR problems are suitable for People Analytics. The overall aim should be to provide actionable insights to the right people at the right time. To do this, HR needs to have a good understanding of what their audience’s priorities are and be able to show how their analysis directly relates to those goals.

LSH: Thank you, Hendrik! It was a pleasure to host you. I hope you’ll continue sharing your experience and resources. I’ll surely continue to follow your work.

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]]> HR Leaders in The 4th Industrial Revolution https://www.littalics.com/new-roles-of-hr-leader-in-the-fourth-industrial-revolution/ https://www.littalics.com/new-roles-of-hr-leader-in-the-fourth-industrial-revolution/#comments Wed, 12 Jun 2019 05:23:23 +0000 http://www.littalshemerhaim.com/?p=1586 You can’t evaluate AI solutions without understanding the basics of practical machine learning and predictive analytics. But you don’t have to be a data scientist for that. It’s like driving a car – you don’t need to be a mechanical engineer to buy or drive your vehicle.

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What new roles do HR leaders have in the 4th industrial revolution? HR departments practice People Analytics to help business leaders to improve performance and growth through insights from people data. But what’s beyond People Analytics? How should HR leaders be prepared for the fourth industrial revolution?

AI changes everything. We have new responsibilities.

We’ve discussed a lot the two trends that contributed to the emergence of People Analytics a few years ago: data democratization – managers demand people’s data to run the business, and data consumerization – employees require to use data for growth, well-being, and positive experience at work, just as they do in other aspects of their lives.

But today, AI can make everybody better in many fields. I use AI to make myself more productive, e.g., I use speech-to-text and text-to-speech to cover content more quickly. Moreover, I collect data about myself in many aspects of my life.

However, I think I’m still among the few who read privacy policies, and I consider them when I choose apps. Data can make us heroes, but it might also destroy us if misused or abused. We leave data traces everywhere: when we drive our cars, watch TV, buy products, consume web content or interact with people on social media. And, of course, we leave data traces with every breath we take at work when we move across offices, write e-mails, manage calendars, learn, conduct our work, or even when we don’t show up to work.

Who owns these traces of data? The regulation now defines it in many parts of the world, but it lags compared to technology. So it is our responsibility, not only as managers or consultants but also as people, parents, and citizens, to understand the rapid changes and make informed decisions. I mean, not only by insights derived from data but mainly informed choices about the usage of data-based apps, which are every app. 

New learning path. New employer rating.

Privacy and ethics are not new in the organizational research field. For instance, we discussed the smallest group we can analyze in surveys more than two decades ago. However, we now have so many new data sources for the workforce, from sensors, smartphones, and desktop apps. As I mentioned, people are not aware of the digital footprints they leave. Therefore, this data might be turned against their interests.

I think it’s time for people to learn how to protect themselves, and this learning path should also be a new responsibility of organizations. We discuss Ethics in People Analytics and HR tech, but we must keep in mind that this is a crucial topic in educating our employees. To do so, learning leaders must already understand this domain thoroughly. Unfortunately, this is not the case in many organizations.

Furthermore, there’s much talk in the HR sector about employee experience. I believe that soon enough, employees will start exercising their rights to data privacy. We’ll see employer ratings based on data transparency and data usage aligned with employee interests – growth and well-being, which is what we mean when we talk about using employee data for good.

New skills. HR people are not there yet.

As AI sweeps HR tech and is introduced to many HR practices in every stage of the employee lifecycle, someone in the organization will have to pick the right solutions for the proper needs. I forecast demand for two new skills in the HR role: Procurement and Ethics. However, if HR people keep procrastinating their up-skilling in analytics, the consequences might be that Procurement and Ethics roles will be filled by someone else in the organization.

You can’t evaluate AI solutions without understanding the basics of practical machine learning and predictive analytics. You don’t have to be a data scientist for that. It’s like driving a car – you don’t need to be a mechanical engineer to buy or drive your vehicle, but you need to know how to hold the wheel and obey traffic rules, so you don’t kill anybody. Therefore, I call HR professionals to start their journey into the data world. And start it today.

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]]> https://www.littalics.com/new-roles-of-hr-leader-in-the-fourth-industrial-revolution/feed/ 2 Changing the Analytic Mindset of HR for Good https://www.littalics.com/changing-the-analytic-mindset-of-hr-for-good/ https://www.littalics.com/changing-the-analytic-mindset-of-hr-for-good/#comments Sun, 02 Jun 2019 11:07:50 +0000 http://www.littalshemerhaim.com/?p=1551 Whatever you do to educate yourselves, ensure that your learning opportunities include experiments with your own data. Master business questions in your organization and your own data, so you can build your company's HR data strategy in the near future.

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(This article is based on my Hebrew “TED” talk at the conference People Geekup Tel Aviv in June 2019. I dedicated this talk to HR professionals who make their first steps on their data-driven journey. Read also my list of Public Speaking).

How to change the analytic mindset of HR for good? I deal with this question for years. I’m a People Analytics consultant and mentor. I help HR teams to leverage people’s data and HR technology to drive insights that contribute to business success. A nice side effect of my activity as an advisor is making HR professionals heroes in their organizations. I’m an applied researcher for more than two decades now – a multidisciplinary professional with a background in Economics, Business Strategy, Psychology, Statistics, Programming, and more. Connecting all dots into a diverse role is not only a millennials theme. It is the reason I started my own business many years ago.

Connecting the dots

 

Here’s a fun fact about me: I’m a photographer artist. I’m also an eternal student, a curious character who learns about ideas and human experiences, and an autodidact in wide-ranging fields of interest – Positive psychology is one example. A few years ago, I realized that principles of Positive Psychology, which I learned from books and lectures, are nicely reflected in my personal experiences as a photographer artist. I started to document those reflections, and soon enough, I introduced to the world a new therapeutic photography method, that was proved to be effective to my students and audience. I called it Focus on Happiness.

If there a single sentence that sums up my entire therapeutic photography method, this would be it: “The view is an interaction of ability and opportunity”. Every picture or frame of our lives is a combination of the things we can do and the circumstances that enable us to do so. As simple as that. This insight is most relevant for me today, as I mentor HR managers on their data-driven journey. The impact of HR professionals in their organization is a combination of what they can do with data, and the business needs, i.e., the circumstances in which they express this ability. Today I’ll share the three key practices (and a bonus one too) that enable such a combination, between ability and opportunity. If HR professionals follow them, their success in impacting the business by People Analytics is guaranteed.

Computers are useless

 

Why not start with the bonus, for a change? The first quote I added to my diverse inspiration list has been attributed to Pablo Picasso, the most vital artist of the 20th century: “Computers Are Useless. They Can Only Give You Answers”. Obviously, Picasso had no clue about People Analytics, but his idea is applicable to all of us in this domain. There is no point in running the most sophisticated analytics or building a shiny dashboard, without the attempt to answer a business question. Start your analytical journey with a business question that involves actionable insights. Picasso was right! Computers can only give us answers. We are the ones who must come up with the right business questions in the first place. Only then, we can proceed with the data, our people data.

Experiment with data – our own data

 

In my recent article, People Analytics – Build the Value Chain, I mentioned that there are plenty of online courses for People Analytics. Some are pretty good; others are nothing but excellent because they enable students to be exposed to the invaluable experience of experts, respected colleagues in this field. However, all online courses lack the opportunity to exercise actual business questions of your company and real people data from your own HR-tech solutions. This can be done only in Intra-organizational training and mentoring. Such training enables up-skilling HR to be more data-driven, and moreover, it may also be the actual foundation of People Analytics projects.

So, whatever you do to educate yourselves, make sure that your learning opportunities include experiments with your own data. Master business questions in your organization and your own data, so you’ll be able to build your company HR data strategy in the near future – move from business question to actionable insights by owning your data sources, storages, analytics and visualized outputs.

Hack #1 – when you afraid to fail

 

I was recently a panelist in HackingHR Tel Aviv. For me, the most exciting moment in the event was when half of the audience raised hands after I asked, “Who works today or is going to start working soon in People Analytics?” I knew most of those faces in the audience, and I realize my contribution to establishing this profession in my country. But it doesn’t mean I didn’t fail. And personally, I know a lot about the fear of failure.

When you start your journey to data-driven HR, be prepared to fail. However, if you create psychological safety in your learning environment, you won’t be afraid to start again. Your failure will only mean that you are not there, yet, but you are getting there. Take Adam Grant’s advice – always question your default solutions and try other options. Or, in the word of a mentee testimonial, in a case study of people Analytics is SMBs – “We could afford to experiment with data, and making mistakes, knowing that we had the support of a professional framework… In our mentoring sessions, but also between sessions, each of us could comfortably ask any question, raise ideas, and make a mistake. Thanks to the openness that was created within the team, everybody felt that we were able to cope with the challenge.”

Hack #2 – when change is difficult

 

Change is inevitable, and that is also true in the HR domain. But the experience of leading a change is hard. Why? Scientific evidence connect the challenge of change to the way the human brain is wired and explains why most change initiatives fail. As I previously mentioned, “A core driver of the brain function is maintaining safety and stability. Therefore, even a beneficial change can be perceived as a threat. When you lead a change in your organization, you directly conflict with your brains’ core needs.”

To overcome this barrier, and help against the reflexive resistance, you need to create new rituals within learning sessions, that would generate a sense of security. While mentoring HR teams, I discovered that rituals are effective: “When meeting agenda and pace of learning are predictable, and when new social norms such as asking questions and thinking out loud are created, people practice openness and curiosity. Familiarity with the setting gives them a sense of certainty and stability.”

 

Build the Value Chain

 

I followed all these key practices – Practice your own data, psychological safety, and rituals – As I built my training program for data-driven HR, which I offer now to organizations worldwide. I help HR professionals to build the People Analytics value chain through sixteen lessons and four milestones. Each HR team can create, with my guidance, its unique rituals. The team members learn by using their own data to solve their business questions, in an experimental environment, where mistakes and failure are welcome as an opportunity to learn. I’m very excited to mold my experience, both my failure and successful case studies, into a structured course that suits each organization, no matter how large or small.

People Analytics - Build the Value Chain - by Littal Shemer Haim

 

 

Data Makes you fly

 

When I started this blog, I chose one of my crane’s photographs for the main page slider. As I previously wrote, cranes are a great metaphor – always on a worldwide journey, with their large flocks, dynamic roles, and inter-dependencies. They are just like us, people in organizations, who are on their journey to data-driven HR. When I wrote on that slider that “data makes you fly”, I couldn’t imagine that in three years I would be recognized as one of the global influencers in the HR-Tech industry, and have such a fascinating career opportunity. In my mind, I only had my career path up to that point. But I also thought about HR leaders who embrace analytics and become heroes in their organizations.

I hope that the HR journey in the data world will last, as the cranes’ endless journey. However, we face such dramatic change now, that may turn everything to other directions. The demand for new skills in the HR role, i.e., the Procurement and Ethics of HR-Tech breathe down our neck. HR people can’t procrastinate their own change. They must up-skill and be more data-driven. I call you to join this journey today.

Featured in the book

Book Cover
"This book is not a typical textbook about People Analytics practices. It offers readers an opportunity to learn and change while enjoying themselves, taking time to contemplate, absorb ideas, and, hopefully, overcome barriers."

"You will find in this book sixteen lessons, organized in four milestones that, from my experience, build the People Analytics value chain."
Littal Shemer Haim
Author

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