employee experience Archives - Littal Shemer Haim

Ethics in People Analytics – The Journey Continues

Littal Shemer Haim — Wed, 17 Jun 2020 12:43:00 +0000

(Reading Time: 4 minutes)

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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Key takeaways from People Analytics World, London 2018 – Part 2

Littal Shemer Haim — Thu, 19 Apr 2018 18:49:31 +0000

(Reading Time: 6 minutes)

The 2^nd day of People Analytics World was a continuation of great professional sessions, and delighting hospitality, as I experienced in the 1^st 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.

The post Key takeaways from People Analytics World, London 2018 – Part 2 appeared first on Littal Shemer Haim.

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

Littal Shemer Haim — Thu, 19 Oct 2017 09:30:16 +0000

(Reading Time: 5 minutes)

(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?

AI (Artificial Intelligence) and ML (Machine learning) are two buzzwords that dominate the HR tech world today. We don’t know yet if there is a bubble in this field or rather a huge influence on management practices. Nevertheless, the common opinion among professionals is that managers will make better decisions, more informed decisions, related to the workforce, by using predictive algorithms that, for example, fit candidates in jobs or let employers know who is at “flight risk.” There are tons of discussions about this subject, but mainly from the organization’s point of view. What I’d like to do now, for a change, is to take the employee perspective.

Should employees worry?

If you tried to land a job lately, perhaps you had a video interview (e.g., by HireVue), or you were asked to play some mobile games (e.g., by Knack). These technologies, which probably offer you a nice experience as a candidate, actually enable organizations to predict your performance in certain roles, basically by pre-exploring reactions of high and low performers in those exact roles. As a candidate, you’ll probably consent to participate in those practices, even though you don’t know exactly what data these machines collect about you and what is the secret predictive model they use backstage.

I’m not saying that predictive models are bad. On the contrary, I believe that in general, a machine that fits the right person to the right job and does so better than a human whose perceptions may be biased is actually positive, not only for organizations but also for employees, since they may have a better chance to thrive in the right roles. However, anyone who has some general knowledge about ML can point to the confusion matrix and demonstrate that algorithms are not perfect, or more precisely, how much imperfect they are.

Why are predictive algorithms not perfect? There are many technical and statistical reasons, but the one that concerns me, in this context, is the possibility that human biases affect seemingly unbiased machines. The promise of ML and AI was that the more information we feed these sophisticated computer algorithms, the better they perform. Unfortunately, when the input data reflects the history of an unequal workplace, we are, in effect, asking a robot to learn our own biases. Garbage in, garbage out, right?

Such unfortunate effects can easily occur in the workplace. For instance, if an analyst explores people who were promoted in the organization for the last decade and decides to use their data to predict high performance, it might result in a model that exclude minorities from predictions about high performance, since maybe minorities were rarely promoted in the past due to social biases or discrimination. This example may be extreme, but it can underline many other subtle possible occurrences.

Who will defend employees?

Defense (and self-defense) starts with awareness. Indeed, the awareness of data protection and privacy is increasing, and influencing society in general, particularly in regulation. Employee rights are broadened these days in the context of workforce data, although not evenly in each corner of the world. In the EU, a new privacy regulation, the General Data Protection Regulation (GDPR), was published lately (and will be enforceable starting May 25th, 2018). It has serious implications for any employer who processes its employees’ and potential employees’ data, whether it is data regarding work environment or internet behavior. Among many issues, the GDPR offers employees additional rights to reinforce control over their personal data, e.g., extended access and rights to be informed about data usage, data transferring, and period of storage. The new regulation is currently covered by legal experts, and anyone who analyzes employee data will soon start to consult legal departments regarding activities that did not require consultation in the past. In Europe, a new organizational stakeholder emerges – a Data Protection Officer (DPO) – and will be involved in analytics projects.

However, in my opinion, compliance with the GDPR is only a starting point. It will surely force awareness of the HR analytics team to privacy issues. But although it aims to protect privacy, I believe it will also influence employees’ behavior, and HR analytics practitioners will have to respond: When people start exercising their rights and request access to their data, People Analysts 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, and therefore, they’ll need long-term planning and more serious considerations. This will move the field of People Analytics forward. The implications for employees and candidates: Transparency! But not only…

Beyond transparency

I believe that eventually, even if it will take a few years, the People Analyst role will include more components of procurement. Analysts will make less programming on their own and be experts in HR tech and analytics solutions. They will learn, for the sake of regulations and ethics, to ask vendors hard questions and be more critical about model accuracy and data privacy, and therefore, they’ll contribute not only to a culture of a data-driven organization but also to a safe work environment regarding employee data. Employees and candidates, for their part, 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.

References:
Kevin Markham, “Simple guide to confusion matrix terminology,” dataschool.io
Stephen Buranyi, “Rise of the racist robots – how AI is learning all our worst impulses,” theguardian.com
Arnold Birkhoff, “9 Ways the GDPR Will Impact HR Data & Analytics“, analyticsinhr.com

Related Course

The People Analytics Journey

An overview of future role of HR leaders in improving business performance by informed decisions about people based on data. People Analytics transforming HR; The Role of People Analytics Leader; Case Studies and Simulations; Emerging trends of HR tech.

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