Littal Shemer Haim

People Analytics, HR Data Strategy, Organizational Research – Consultant, Mentor, Speaker, Influencer

Ethics in People Analytics – The Journey Continues

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. I'm creating a comprehensive resource list that will be updated monthly.
Photography by Littal Shemer Haim ©

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.

Littal Shemer Haim

Littal Shemer Haim

Littal Shemer Haim brings Data Science into HR activities, to guide organizations to base decision-making about people on data. Her vast experience in applied research, keen usage of statistical modeling, constant exposure to new technologies, and genuine interest in people’s lives, all led her to focus nowadays on HR Data Strategy, People Analytics, and Organizational Research.

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