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In Part 1 of this series, I discuss how Ethics change People Analytics practices and what happens when People Analytics becomes workforce surveillance during the COVID-19 pandemic times. In this part, I ask what happens when caring about employees intrudes onto personal lives and explore People Analytics Ethics Guidelines and the future. This series is a follow-up writing inspired by Brainfood Live, in which I was honored to participate lately. Some quotes and stories in the series are based on my monthly review of workforce AI Ethics resources.

What happens when caring about employees becomes an intrusion onto personal lives?

As workforce data sources diverse and includes indicators about attitudes, behavior, movements, and physical responses, based on many technologies, e.g., analyzing what people say or write or how they interact with digital tools, the question about the boundaries between work and non-work arise. Would anyone agree to volunteer to offer their DNA in the recruitment process? As much as it sounds creepy, organizations started to use biometric data to understand work productivity. Even when done voluntarily, the very essence of employer expectations, and certainly in times of crisis when everyone strives to maintain jobs, raises questions. For that reason, I believe that we are about to see a new kind of social activity, for example, in unions. Here are two stories to demonstrate this.

One company harnessed AI and fitness-tracking wearables to understand how the work and external stressors impact 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? One controversial approach was to combine ML with wearable devices to understand how lifestyle habits and external factors impact employees. 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. Balancing work and home life benefit mental health and wellbeing.

Understanding performance and wellness is, clearly, an interest of both employees and employers. However, it initiates 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? And what if 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 is always a self-selection bias among employees, so the beneficial results are not equally distributed.

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? One app, WeClock, enables employees to track their data and share it with unions to ensure that their whole online existence doesn’t become their employers’ property. Data from employee surveillance used to boost productivity strengthen the position of power that employers have over employees. Regulation for individual rights to data does not offer sufficient remedy yet. 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 to employee data. This app enables employees to track, and share with their unions, things like how far they must travel to work, whether they’re taking their breaks, and how long they spend working out of hours. It is an interesting attempt to provide a source of aggregate data about critical issues affecting employee wellbeing.

What is the future of People Analytics? What to do about it today?

Businesses are requested to obey the regulation. Managers must comply with organization policies. But compliance doesn’t guarantee that we are always doing the right thing. We may experience conflicts between the business policies and our values. Fortunately, organizations have the means to bring those kinds of conflicts into a discussion, e.g., in specialized committees. Some organizations offer their personnel educational opportunities in the domain of Ethics. However, most managers lack a basic understanding of workforce AI tech tools. Unlike general incidents of ethical issues raised when policies and values are conflicted, AI ethical issues involving bias are less noticeable. The responsibility to the ethical use of AI is still perceived as belonging to the vendor side. Eventually, this state of affairs will change, thanks to employee expectations, new roles within HR, and procurement standards.

Employee expectations will change. People will rate employers, in addition to employee experience, based on the ethical use of data. Therefore, People Analytics leaders will fit the right tools to their organization’s business questions according to values and culture. AI ethics are new skills. HR professionals should educate themselves first. When feeling secure, people will be receptive and enthusiastic to cooperate with AI and data usage to influence their career path and work. Unfortunately, most employees and candidates still lag in understanding the consequences of the increased use of data. Organizations, mainly learning functions within HR departments, have a lot to do to educate the workforce to be informed participants in those practices.

New roles will emerge within HR departments. 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 ethical use of AI-generated talent data to prevent potential harm. HR will establish new practices in collaboration with the legal team to ensure the algorithms’ results are transparent, explainable, and bias-free. They will start considering the balance between stakeholders in the organization by asking how technologies serve both employers and employees beyond the apparent discussion about what technologies they should be using.

A recent survey revealed the shift in the HR sector’s mindset and creatively described 21 HR jobs of the future. It depicts hypothetic future HR roles responsible for crucial issues such as individual and organizational resilience, organizational trust and safety, creativity and innovation, data literacy, and human-machine partnerships. 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.

Procurement standards will change. Research institutions already started to create frameworks that support the responsible development, deployment, and operation of machine learning systems. Volunteering domain experts articulate “Responsible Machine Learning Principles” that guide technologists. They set templates that empower industry practitioners who oversee procurement to raise the bar for AI safety, quality, and performance. While ethical frameworks are still hard to implement because there is not much technical personnel that can offer high-level guidance, in the future, we will start to see AI ethics principles in organizations’ metrics, e.g., for fairness and privacy. I believe that regulation will follow eventually. The European Union decided to stricter rules on cyber-surveillance technologies, like facial recognition and spyware. Maybe it is only a step towards transparency, or it implies a future impact on organizations’ practices.

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The global Covid19 crisis brought a significant portion of the workforce to work remotely. According to Gartner, 88% of the organizations worldwide made it mandatory or encouraged their employees to work from home after COVID-19 was declared a pandemic. Therefore, we witnessed an acceleration of the digital transformation of work processes and measures. Many organizations started to record and leverage new workforce data sources to enhance productivity by understanding employee behavior and sentiment. However, with the evolving practices of People Analytics, new ethical concerns emerged.

I was honored to be a guest in Brainfood Live and discuss those concerns with my respected colleagues Andrew Marritt, CEO at Organization View, Adam Gordon, CEO at Candidate.ID, and Hung Lee, the show curator and CEO at Workshape.io. Inspired by our conversation, I share some ideas about this topic in the next two blog posts. In Part 1, I discuss how Ethics change People Analytics practices and what happens when People Analytics becomes workforce surveillance. In Part 2, I ask what happens when caring about employees becomes an intrusion onto personal lives and explore the future of People Analytics. Some of the stories I bring in these two blog posts are based on my monthly review of workforce AI Ethics resources, which I offered several months while exploring this topic.

Ethics change People Analytics practices, but slowly.

For a few years now, I keep saying that People Analytics leaders won’t be in charge of the programming, but instead, they will be in charge of the procurement in HR-tech and analytics solutions. For the sake of regulations and ethics, they will learn to ask vendors hard questions and be more critical about model accuracy and data privacy. They will contribute to a culture of a data-driven organization and a safe work environment regarding employee data.

People Analytics roles are changing as data ethics awareness increases, i.e., knowing what is good or bad and practicing 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. Compliance with the GDPR and other regulatory issues was only a starting point. It inevitably forced awareness of People Analysts to privacy issues. But someday, people will start exercising their rights and request access to their data, ask to correct or erase their data, and ensure that their employer processes only the personal data necessary for specific purposes.

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 professionals, called The People Analytics Journey. 25% of my training program is dedicated entirely to practices of procurement and ethics in People Analytics.

My takeaway from the experience I had in training 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. But 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. A basic review is insufficient for understanding it to the level of dealing with potential Ethics risks. Yes, I wrote some guides and tried to explain 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 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 started the comprehensive resource list that I mentioned above. I decided to include four categories: Ethics in workforce strategic thinking, Ethics in workforce AI practices, Ethics in product reviews, and Ethics in a social context. I hoped that such categorization would facilitate learning in the field.

What happens when People Analytics becomes surveillance?

The title of Brainfood Live show implied some critics about People Analytics solutions. Indeed, the volume of data available to understand and predict employees’ behavior will continue to grow exponentially, enabling more opportunities for managing through data. The systematic attempt of People Analytics to make organizations more evidence-based will continue, using technology including employee listening tools, monitoring safety and wellbeing, biometric data that people willingly share, and of course, performance or productivity measures.

In the pandemic times, thousands of companies started to buy surveillance software that takes webcam pictures of their employees and monitors their screenshots, login times, and keystrokes. Dystopian descriptions go on and on. Some remote employees are photographed along with their desktop screenshots every few minutes. Others are tracked while browsing the web, making online calls, posting on social media, and sending private messages. As creepy as it may sound, phones, sensors, wearables, and IoT can detect and record our moves. Not surprisingly, workers are concerned about privacy and security. When such tools become mandatory, employees may also worry about using their data unexpectedly and even turning it against them.

The temptation to use people’s data against them is real, for instance, when their measures indicate low productivity. But employees reciprocate. I would not be surprised to hear about employees who don’t feel trusted and find creative ways to avoid the surveillance software. Organizations need to tackle ethics and be transparent to build and maintain employee trust in the use of their data. Business leaders must ensure that there is no conflict between employer and employees’ interests. Therefore, HR departments must lead the conversation that addresses employee trust, corporate responsibilities, and new technology’s ethical implications.

The surveillance solutions may be perceived to provide employees incentives to maintain their productivity. However, psychological experiments reveal that surveillance solutions might lead to the opposite consequence. Spying on home-working employees may be a bad idea, contrary to what economic theory would predict. People’s motives are complex. Alongside material payoffs, people value autonomy and reward trusting employers. The debate about remote workforce surveillance should not focus only on privacy and the blurred boundaries between work and non-work, as these perspectives are not comprehensive enough to understand the employment relations. Surveillance technologies might be the wrong solution for boosting productivity because they signal distrust and reduces intrinsic motivation to perform well.

However, if a surveillance application is installed in your organization, there are things to do to track your employees ethically, starting with these simple steps: Accept that remote work is here to stay; Engage the workforce to reach an agreement on which business activities require monitoring and ensure that the benefits of doing so are understood; Ensure you introduce sufficient safeguards to prevent abuse; Be aware that discrimination can occur despite precautions put in place; Rebuild the trust levels that existed in office settings. Another recommendation is to set goals and communicate expected outcomes while offering employees greater autonomy and collaborating tools.

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

AI changes everything. We have new responsibilities.

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

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

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

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

New learning path. New employer rating.

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

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

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

New skills. HR people are not there yet.

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

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

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(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.

The Syllabus

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