strategy Archives - Littal Shemer Haim https://www.littalics.com/tag/strategy/ People Analytics, HR Data Strategy, Organizational Research - Consultant, Mentor, Speaker, Influencer Sun, 21 Jul 2024 09:11:25 +0000 en hourly 1 https://wordpress.org/?v=6.7.2 https://www.littalics.com/wp-content/uploads/2021/02/cropped-grey-32x32.png strategy Archives - Littal Shemer Haim https://www.littalics.com/tag/strategy/ 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.

The post Ethics in People Analytics and AI at Work – Best Resources appeared first on Littal Shemer Haim.

]]> 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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]]> https://www.littalics.com/changing-the-analytic-mindset-of-hr-for-good/feed/ 2 Key takeaways from People Analytics World, London 2018 – Part 2 https://www.littalics.com/key-takeaways-from-people-analytics-world-london-2018-part-2/ https://www.littalics.com/key-takeaways-from-people-analytics-world-london-2018-part-2/#comments Thu, 19 Apr 2018 18:49:31 +0000 http://www.littalshemerhaim.com/?p=983 Eight key takeaways from the conference second-day sessions, case studies, demos and panel.

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The 2nd day of People Analytics World was a continuation of great professional sessions, and delighting hospitality, as I experienced in the 1st day of the conference. I continued the fascinating exploration of People Analytics leaders who develop their field and provide their organizations with valuable tools that enable actionable insights. In this blog, I share my key takeaways from the conference second-day sessions, case studies, and demos, in which I participated.

#1. Keynote:
People Analytics role in navigating into the future of work

Katie Minton and Neera Ridler-Mayor, both Directors in People & Workforce Analytics at Deloitte, discussed the role of People Analytics in navigating the future of work. They presented an update of research about the future of work and its impact on HR. The workforce of the future will be very different than it is today, as artificial intelligence and robotics advance at pace will enable more work to be done by smart machines. They explored the role that HR and People Analytics need to play to navigate this disrupted landscape, and in particular in the formation of the “Social Enterprise”, which emerges when both the level of collaboration and internal agility and the level of external focus rise. They described the wide future role of People Analytics, that will include areas such as: optimizing business models, dealing with inclusive workforce and environment, driving the future service of HR, engaging in open talent economy, providing data points to direct opportunities for positive disruption, i.e., productivity. They recommended design thinking for People Analytics solutions and using People Analytics approach for People Analytics solutions, i.e., explore colleagues’ needs and demands. Their four keys to become a hero are: think big, start small, act fast, and stay human.

#2. Disrupt:
Be a game changer by techniques and attitudes that are being used in Marketing

Luk Smeyers, Co-Founder and CEO of iNostix by Deloitte guided People Analytics leaders to think like their colleagues from Marketing and apply Customer Experience Analytics to the employees. Smeyers encouraged People Analytics practitioners to get outside of the ‘dark room of HR’, aka their silos, and to use five techniques: hyper-segmentation, A/B testing, investment elasticity, uplift modeling, and statistical forecasting. He described a four-level model of HR transformation, and suggested HR to move first towards (and keep doing) “datafication” and business integration. The next steps would include integration with employees’ digital world and skills of the future. Smeyers considered GDPR a “present from Heaven”, which demands new culture: content-based, co-creation with employees, transparency and employee empowered. He also offered some valuable tips for “starting tomorrow”: use cross-functional team, do iterative work, and avoid silos by negotiating with your provider to access the data behind the dashboards.

#3. Demo:
Create a business impact with strategic workforce planning

Kai Berendes, an Executive Partner at Dynaplan, demonstrated how to create a business impact with strategic workforce planning. While analytics is usually focused on workforce supply, his strategic workforce planning uses holistic models that include both demand and supply, and bridge between business strategy and future workforce, by customizable demand models, a link between job families and skills, and between planning process and technology. Berendes demonstrated how to explore the impact of today’s decisions about people on the future, by simulation with the product. He explored supply dynamics step by step and then moved to demand, to find what roles are missing or no longer needed, using a graphical approach. He also demonstrated how to apply policies to the system, and receive a recommendation, e.g., what to do to close gaps, how to choose between internal or external workforce, and how to explore bottlenecks, competencies behind jobs, and more.

 

#4. Impact:
Understand employee experience to address turnover intentions, and prevent regrettable losses before they happen

Vanessa Lammers, Director of Global People Analytics & Insights in Nestlé Waters presented a proactive approach to enhance the employee experience and improve retention: the “Engagement Check-Ins”. Since exit surveys are not actionable, Lammers offered an alternative methodology to keep high-risk employees: from a predictive turnover model to a targeted engagement interview, to specific front-line manager intervention. She reviewed how her company leveraged both a predictive model and workforce planning approach to conducting Engagement Check-Ins and shared an online tool that aggregates employee feedback and captures action planning, thus enables a real-time pulse of the organization. Specifically, she described in details an original form that was used after check-ins conversation, for capturing open-ended answers quantitatively, and a dashboard that encouraged managers to fill them. Lammers emphasized the fact that since organizations are social systems, some of the most meaningful and actionable data is qualitative, and meaningful insights come from simply talking to people. Furthermore, managers can play a critical role in employee engagement and retention.

#5. Strategy:
Identify problematic people processes which are causing business problems

Max Blumberg, Ph.D., Visiting Professor at Leeds University Business School, explained how to identify the real challenges of the organization. By generalizing a case study related to a company’s sales, Blumberg showed how important is to know where to focus, to impact the business with People Analytics. When done right, we get organizational capabilities that end-up with KPIs, translated to money, he explained. However, too often, People Analytics addresses other issues, mainly people process relevant to HR like attrition, recruitment, and learning and development. Organizations generate value by investing in productive assets. However, in regards to people, while calculating the costs is easy, it is difficult to assess the value side of the equation. Analytics that address people process seldom provide senior general managers with the guidance they need for allocating budgets between competing assets to achieve their desired business outcomes. Blumberg stressed the importance for People Analytics to address business problems rather than just people problems and suggested a methodology for identifying problematic people processes which are causing business problems, by communicating with senior managers via a survey. This business-driven approach, according to Blumberg, is a good alternative to a data-driven approach.

#6. Disrupt:
Absence metrics enable to compare different operational employee groups

Caroline Williams, Manager of People Analytics in British Airways, demonstrated how the company drives wellbeing and positive behaviors in the workplace through absence analytics. This customer-serving organization depends on the wellbeing of their people to deliver high standards of customer service. One factor in supporting employee wellbeing is the management of absence. However, the causes of absence are often complex and challenging, as are the impacts on costs and productivity. The company uses analytics to better understand absence. The role of People Analytics in monitoring attendance was to create agreed definitions, define metrics, and improve analytics confident. The impact was a self-serve metrics, improved interventions, and integration of data into decision making, towards the goal of a well, happy and engaged workforce. However, it was a long-term project, with two years of validating data and getting everybody to roll the same reports.

#7. Strategy:
Create value by People Analytics practice with limited resources

Michael Tocci, Ph.D., Global Leader Talent Analytics & Insights in Procter & Gamble, offered guiding principles for creating value for the business with People Analytics, and demonstrated how an analytics project resulted in saving millions of dollars, by exploring expatriates costs globally, and considering recommendation, e.g., reducing expats in certain countries, looking for roles that can be located in lower cost locations, exploring necessity of senior managers, and exploring high potential and low performers among expats, and more. He presented how to create a People Analytics function with limited resources (time, budget, and people), and how to create value based on data-informed insights in HR. Specifically, he emphasized that fancy stats are not necessary and credibility is the key.

#8. Closing Panel:
How does HR stay relevant, in the world of automation?

The closing panel was led by David Green and included Steve Bianchi, VP People Operations in Improbable, Nicky Clement, VP HR, Organization Effectiveness, Performance & Analytics in Unilever, Jordan Pettman, Global Head of HR Data, Analytics & Planning in Nestlé, and Jim Matthewman, Consulting Director at Talentspringboard. It addressed organizational preparations for Digital Transformation. Technology is touching every aspect of the organization, and it is redefining the way people work, interact, report, manage and progress in their careers. With the audience contributions, the panelists addressed questions about culture readiness, benefits of digital transformation, data ownership, GDPR, and the relevance of HR in the world of automation. The panelist agreed about the lack of data talent and the misunderstanding of Analytics role. They stressed the importance of customer approach in the digital transformation and expressed their concern about the abuse of the digital world. In a blurred future of work, where gig economy is growing, skills demand is changing, and machine capabilities are leveraging, it is time to go back to creativity, innovation, and the emotional part of our brain, that machines don’t have. People and HR will have to offer a different value proposition, which will include readiness to learn, curiosity, and willing to transform to stay relevant. The new psychological contract will be value for data, but transparency and ethics regarding people data will be necessary.

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]]> https://www.littalics.com/key-takeaways-from-people-analytics-world-london-2018-part-2/feed/ 1 Key takeaways from People Analytics World, London 2018 – Part 1 https://www.littalics.com/key-takeaways-from-people-analytics-world-london-2018-part-1/ https://www.littalics.com/key-takeaways-from-people-analytics-world-london-2018-part-1/#comments Wed, 18 Apr 2018 20:04:51 +0000 http://www.littalshemerhaim.com/?p=974 The growing importance of data-driven HR was well reflected in the conference. this article covers ten key takeaways from the conference's first day sessions, case studies, and demos. The next blog covers the conference on the 2nd day.

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People Analytics World is a leading European annual conference on HR Analytics, Workforce Planning, and Employee Insight, in which I was privileged to attend in April 2018. I traveled to London with huge expectations, to learn more about the contribution of People Analysts, which are now becoming an essential part of HR groups across all industries. The growing importance of data-driven HR was well reflected in the conference’s attendees, both speakers, exhibitors, and delegates. My experience in the event exceeded my expectations. Thanks to the professional sessions, the delighting hospitality, and the great chairing of David Green, I had a wonderful opportunity to explore how HR leaders reinvent their domain, train themselves and their organizations to be prepared for the age of data, and get new tools that enable them to provide insights to maintain a competitive edge.

The conference program was challenging. It was split between three parallel tracks: Strategy, Impact, and Disrupt. I had a hard time choosing between lectures since I found all the speakers and topics relevant and most interesting. Fortunately, the conference organizers offered interactive tools that helped me to plan my agenda. In this blog, I share my key takeaways from the conference’s first day sessions, case studies, and demos, in which I attended. My next blog covers the conference on 2nd day. In future posts, I may cover many sessions I missed, with some references that I read to recover my horrible FOMO (fear of missing out).

#1. Keynote:
Demonstrate how staffing creates an organizational capability that contributes to competitive advantage

Alec Levenson, a Senior Research Scientist at USC Marshall Center for Effective Organizations, suggested how to make People Analytics a part of organizational strategy. He started with some bad news: we are struggling with data quality while buying some shiny tools, but it won’t lead us to success, since no one in the organization looks at the bigger picture, and own the end to end analysis. Sometimes people in different departments work at cross purposes with each other, yet think that they are pursuing the company’s best interests. While we talk about ROI (Return of investment), this is a short-term financial return, in comparison with a competitive advantage, which is a long-term financial return. What is the return in the domain of people? We need to build capabilities that eventually will show an impact on productivity and profitability. Our metrics should be designed in three different levels: job, team, and business unit. However, we should start with the business level and not job performance, as we got used to. According to Levenson, the business performance causal model goes from the bottom up. A failure to align these three different levels will end with partial success in goals achieving. Productivity is not an individual issue. We should demonstrate how staffing creates an organizational capability that contributes to competitive advantage. Levenson invited us all to learn more, in his new book “Strategic Analytics: Advancing strategy execution and organizational effectiveness”.

#2. Keynote:
Learn some practical lessons from super-intelligent elite sports teams

Bernard Marr introduced his brand-new book “Data-Driven HR”, which offers practical guidance to HR professionals in leveraging the value of data available at their fingertips. Like elite sports, organizations have a huge amount of data, structured and unstructured, on the cloud and on devices, which will eventually change work. In sports, real-time analysis is done by AI tools that replace people who previously coded data, e.g., cameras or sensors that are used to record every move of athletes and teams. Analysis based on NLP enables to produce automated sports reports and replace journalists. Braking data silos on the cloud enables to optimize learning and offers a huge amount of intelligence related to sports players. Data analysis is not native to the sports domain, so new partners are needed, along with new considerations of data security. Marr claimed that most HR teams are data-rich but insight-poor. He outlined a path to more intelligent HR teams and discussed practical lessons that HR teams can draw from super-intelligent elite sports teams: Find future roles in using data. Design data strategy, i.e., contribute to key goals with data. Use the right data instead of big data. Build new capabilities related to data. Create trust and transparency so people will be ready to give access to data for the value they’ll get. Consider data security and ethics. Consider data diversity and use a variety of sources, e.g., devices, social networks, sensors, videos, etc. Move from report about the past to real-time and predictive analytics. Move to process automation while focusing on a strategic role. Find the right partners in new places, e.g., crowdsourcing and professional communities.

 

#3. Demo:
Transform real-time engagement analytics to personalized management insights

John Murray of Peakon discussed how to combine communication, processes, and technology to build momentum in the organization, creating an environment where employees are engaged, productive, and working towards the same goal. He stressed that Engagement is not just HR’s responsibility. However, there are benefits and challenges in moving to a real-time employee experience analytics model. In his demo, he showed how to build a tailored action plan based on analytics at all levels of management. In particular, managers can own their personal performance by dashboards that provide them with an overview of their team’s engagement data. As an admin, HR can control managers’ access to data and functions, and offer different dashboards for junior managers and senior leaders. The dashboards highlight findings and priority issues, thus help managers to respond effectively. Engagement scores are tracked over time, encouraging managers to continue their ownership over data and performance. Employee anonymity is a concern. Therefore, the platform limits manager access to real-time feedback until a sufficient number of employees have responded. The platform also offers managers the ability to communicate with employees directly, while preserving employee anonymity. The product includes many other features, e.g., benchmarking, employee conversations, Text Analytics, and many more to explore.

#4. Strategy:
Shape HR priorities using analysis and innovative experiments

Brydie Lear, Global Head HR People Analytics in ING, and her colleague Eva Oudeman, Lead Data Scientist People Analytics, covered how the Bank has been building a mature analytical team, moving away from one-off analyses and experiments, towards being asked by senior management to support strategic initiatives. Not only delivering high profile projects but even shaping strategic (HR) agenda using the output from innovation experiments. They described ambitions, the journey so far, the core services, key pillars for success, and how advanced analytics support strategic data-driven decision making. Their portfolio of analytics products includes – Hiring algorithm that reduces manual support and selection bias, by automatically matching CV’s to job profiles and predicting high performance; Continuous listening process for frequent feedback, to understand employees’ perception of strengths and key issues; Diversity projects, for an in-depth understanding of current and expected diversity situation, providing data-driven approach and dialogue on goal setting and realization; Reward solutions, for accurate financial and non-financial recognition, and forecasting, maximizing the return and effectiveness of incentives; Top talent performance, to identify top performers and potential talents, allowing for focused approach and maximum return; Voluntary attrition analysis, for predicting talent at risk to leave ING, enabling pro-active actions to reduce the risk and costs of replacing these key resources. Future add-ins of this portfolio will include team performance, for an in-depth understanding of key ING specific drivers for high and low performing teams, and hidden network understanding, to boost business performance.

#5. Impact:
Think about analytics in the space of competency development and validation

Subhadra Dutta, Head of People Science and Analytics in Twitter, reviewed how the company has been mapping individual competencies and performance stats, and their relation to organizational performance, using employee data and operational KPIs. Competencies are abilities or attributes, described in terms of behavior and key to effective performance. There is a data-based approach for developing and validating manager and individual competencies, and Dutta illustrated how operational outcomes are used to develop and validate these competencies. Dutta emphasized the importance of understanding what keeps employees, what makes them leave, and how the organization can help them to do their best. People join organizations for eliminated time. Therefore, it is essential not only to ensure to offer them great employee experience, but also to measure what really matters in their performance to other things in the business, and validate that. She demonstrated how traditional methodology in psychology research is relevant to current practices in People Analytics.

#6. Demo:
Align people processes that benefit from advanced analytics and adopt an agile mindset

Pauli Dahlbom, Founder of PeopleGeeks, presented a super interesting demo of advanced analytics deployment and success in Musti Group, a leading pet supply chain in the Nordic countries, which has 260 stores in Finland, Norway, and Sweden. The HR group of Musti applied Machine Learning-based sales forecast models, to optimize workforce planning and to automate their scheduling process. Their predictive model proved to work more accurately than the previous sales budgets that the business had built, and it has a huge impact: It optimized contract types and contract hours and enabled significant yearly savings. It reduced the participation time of hiring managers and improved the quality of new hires. The project also enabled to redesigned workforce scheduling and planning activities, to build predictive optimization model to align staffing hours to match expected traffic and to identify top-performing teams and individuals and target them to most important shopping periods.

#7. Keynote:
Challenge yourself with the opportunity of strategic position by a strong evidence base

Peter Cheese, CEO of CIPD talked about the opportunities and challenges for HR when embracing analytics. From productivity to cybersecurity, to innovation and agility, diversity and inclusion, the issues facing business are about people, and yet, the HR base of data, evidence, and insight are fragmented and inconsistent. HR lacks common frameworks and language, focus on this domain not necessarily in the right ways, and perhaps doesn’t have the right capabilities. To put this in simple words, “HR has too much PowerPoint presentation and not enough Excel files”. HR must understand the outcomes and insight needed, recognize the opportunity of collecting information about people, and built a momentum using AI to analyze and interpret it. However, HR has also profound challenges in raising ethics and trust from the people. Their stakeholders are not only the investors, but also the people, the environment, and society as a whole.

#8. Strategy:
Look at data and analytics across the organization from a perspective of collaboration

Michael Cox, Head of HR Business Excellence, Technology & Analytics, and Jordan Pettman, Global Head of HR Data, Analytics & Planning, both from Nestlé, presented the company’s journey to strategic data partnerships across the organization. In this globally distributed, complex, and ever-changing business, the HR team had not traditionally leveraged and managed their data to drive results out of analytical approaches to problem-solving. These two professionals positioned People Analytics as a business enabler, not an HR division, and offered examples for global People Analytics functions and practices. An important lesson is to use the same terms within the finance and HR departments. i.e., use the same numbers for the same reasons. Although these professionals managed to offer a global report catalog, they stress that there is no need for perfect data, and there is no single way to do it. Furthermore, since HR practitioners don’t know yet how to get the right People Analytics talents, it is essential to turn to colleagues from other departments and to connect with analysts who like to share their practices on collaboration platforms.

#9. Impact:
Break out common routines of Employee Engagement analysis and produce actionable insights that worth the executives’ attention

Laurie Bassi warned the audience that it is easy to plow deeper and deeper into employee engagement data, but lose sight of what it means for the actual performance of the business. Employee Engagement measurement has all too often over-promised and under-delivered. Bassi focused on practical ideas to get more value from the investment in Employee Engagement Analytics: Optimizing its “real estate”, doing simple but clever analytics beyond one-size-fits-all, focusing senior management on the key findings and making it easy for managers to act. Bassi offered five essential steps for valuable employee engagement surveys: ask the right questions, link survey data to outcomes data, “mass-customize” findings and recommendations, make it easy to understand, and point managers to solutions. A good starting point would be Sales, because these departments usually have good data, and executives really care about them. Survey questions can also be a proxy to business results. Bassi suggested some axioms for People analysts to repeat daily: The importance of the problem you are working on is approximately inversely related to the mathematical sophistication of the techniques needed to solve it. Outliers are your friends. Less is more. And finally, if you like to appear to be the smartest person in the room – get over it!

#10. Impact:
Set your sights on using your capability to turn HR into a profit center

Patrick Coolen, Head of Strategic Workforce Planning & Advanced Analytics in ABN AMRO shared his experience on set up, governance, methodologies, and outcomes of People Analytics. He covered future challenges: concepts of ‘instant’ analytics and continuous listening. He also offered ideas about opportunities to use People Analytics to generate revenue and direct ROI. An interesting perspective Coolen presented was the idea to start your People Analytics journey at the top of the developmental pyramid, assuming that if you could do that, you could do it all. Another interesting idea he presented was the team dashboard for the research portfolio, which enables us to link a variety of variables from different research to a few outcomes across the business.

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]]> https://www.littalics.com/key-takeaways-from-people-analytics-world-london-2018-part-1/feed/ 3 What do HR Data Strategists do for Companies? https://www.littalics.com/im-an-hr-data-strategist-what-do-i-do-for-companies/ Wed, 28 Sep 2016 05:48:00 +0000 http://www.littalshemerhaim.com/?p=277 An HR Data Strategist can be an insider or consultant who leads or participates in planning and managing how the company’s HR data is gathered, handled, used, and protected.

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People analytics has been gaining speed since 2016. HR departments are becoming an essential source of data in organizations, and more people’s data are used to improve the business. While implementing more predictive analytics and sophisticated analytics, HR is becoming a data-driven domain. Hence, new positions, requirements, job descriptions, and projects emerge. A prominent role nowadays is HR Data Strategist.

As an HR Data Strategist, I bring Data Science into daily HR activities to guide organizations in decision-making about people on data. Basically, a person in this role can be an insider or consultant who leads or participates in planning and managing how the company’s HR data is gathered, handled, used, and protected. There are four major parts to this role:

1. Define and answer the right questions

The HR Data Strategist sees the big picture of data priorities for the company’s HR activities. This person highly understands the data strategy of the organization’s business, particularly its implementation in the HR domain. Thus, she can lead HR managers to ask the right questions, determine what data are needed to answer those questions, and tactically assist in reaching the answers.

2. Define and apply ethical guidelines for data strategy

The HR Data Strategist leads or participates in implementing data strategy within the organization. She is required to define business cases and manage technical projects around them. Regarding data, this person can be considered the organizational ethical guide, being involved or in charge of not only data accuracy but also data security and privacy policies.

3. Identify opportunities through data

Because of her unique relationship with data, the HR Data Strategist can discover new opportunities through data. She may recognize new values in HR data. Based on information gained through data initiatives, she may also contribute to increasing revenue or decreasing costs in HR and the organization as a whole.

4. Lead and inspire a data-driven culture

The HR Data Strategist is a new organizational leader who inspires managers and employees to a data-driven culture. She convinces managers and employees of the importance and the business value of data. Since her activities influence the organization as a whole, they are naturally discussed at the C-level management.

The HR Data Strategist in a multidisciplinary role. This person must have both a technical background and a deep understanding of business and HR professions. In my activities in organizations, I offer my vast multidisciplinary experience, which mainly includes: statistical background, best practices, and methodologies for organizational research, familiarity with data modeling and visualization techniques, experience in leading organizational projects with technical teams, business experience in the HR field, and strong communication skills at C- level.

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