People vs. Machines: The Ongoing Ethical Concerns of People Analytics
HR analytics, also referenced as people analytics, workforce analytics and talent analytics has been on a slow, steady climb for many years. As this capability matures within the HR function or a centralized analytics function, ethical concerns come to the forefront.
When you put a sensor on a machine, it rarely objects. When you put a sensor on people… that’s a completely different story.
In my former career as an engineer, we studied the parameters of manufacturing equipment using the "design of experiments" or DOE. Once we ran our experiments, we could optimize the settings of the machine to maximize output and the quality of that output. We could use our experimental data to optimize the product being manufactured.
With people, you can't turn the dials and change behaviour overnight. Sure, you can put some metrics in place to try and steer behaviour, but the unintended consequences can be massive. In the field of people analytics, it is important to look ahead and think about potential consequences of a behavioural study. Before commencing a study of employee behaviour, contemplate the outcomes.
In some situations, studies have been conducted where there was no possibility of a positive outcome. The employee experiment was doomed from the beginning. Take the following newspaper company as an example.
This newspaper company recognized that its reporters spent most of their time in the field, i.e., not in the office. This company wanted to determine how often reporters used their desks to see if there was an opportunity to consolidate office space. If the requirement for office space could be reduced, the company could reduce its costs.
The company placed sensors under the reporters' desks to detect when they were present. What happened next? The reporters found the sensors and removed them. The experiment was over. For the company, they did not have the answer to their office usage questions and a great deal of money was wasted on the purchase of sensors and arranging the experiment with a third-party.
This is why it's crucial to recognize that there can be a Catch 22 in people experiments. If the company had communicated their experimental intentions, the employees likely would have modified their behaviour and spent more time at their desks. This would have produced inaccurate data in the experiment. Since they didn't communicate their intentions, having the employees discover the sensors destroyed the trust between employer and employee. This was an experiment which could never have a successful outcome.
Data Privacy and Data Transparency
With the exponential expansion of data production, particularly with data attached to individuals, companies and regulators now have to contemplate data ownership. In the EU, we saw the creation of the GDPR regulation in an attempt to balance privacy and transparency. In California we’re seeing the launch of consumer privacy laws. We can expect to see more of these regulations spread across the globe.
Benefits vs. Concerns
There are also touchy situations attached to many well-intended initiatives. Companies have thought of providing objects such as wearables. The company’s though process goes something like this.
· If we provide wearables to employees, they will become more aware about their own health data.
· If they are more aware of their health data, they will take action to improve their health.
· If they can improve their health, then the cost of health claims will go down.
· If the cost of employee health claims goes down, then companies can reduce the monthly premiums employees pay for healthcare benefits.
This is a good plan in theory, but unfortunately there's a Catch 22 here. Here’s why. The employees who would benefit the most from knowing their health data are those that are in poor health. They have the greatest opportunity for improvement at that point in time. These employees are the same subset of employees who would most fear that their "bad health data" would be used against them. Just as smokers pay higher health premiums, these employees wonder if they would be penalized with higher health care premiums if their wearable data becomes known.
What People Analytics Leaders Need to Think About
While I present in this article, what companies need to think before conducting a people experiment, who is ultimately responsible for executing that thought process? Is it the responsibility of the people analytics leader to facilitate this discussion? If the request for the experiment came from an HR leader, would a people analytics leader lead the though process and advise that leader when experiment should not be conducted? Should we involve our in-house psychology experts?
Regardless of who leads the discussion, it will likely be a combination of employees that needs to conduct the assessment of whether there is value in the proposed experiment. Unfortunately, many people analytics leaders are analytics experts whose focus is only on the technical skill set of the experimental request.
Many analytics teams are failing to ask the following questions:
What can happen that will make our experiment fail?
Will we destroy employer-employee trust by conducting this study on our people?
Does the VALUE of the outcome justify the risk and cost of the experiment?
HR analytics, unlike operational analytics, comes with a greater risk of ethical concerns. Studying people is always going to be harder than studying machines because people are less predictable are far more difficult to alter. Today, the responsibility for assessing ethical concerns in people studies has no specific place within most companies. It’s a consideration that now needs to be inserted into the workflow of all people studies.
[This article was originally published in HR Zone]
Tracey Smith is an internationally recognized business author, speaker and analytics consultant. She is one of the most highly respected voices when it comes to business analytics and HR analytics. She is the author of multiple business books and hundreds of articles in a variety of publications. Tracey has worked with and advised organizations, both well-known and little-known, on how to use data analytics to impact the bottom line. If you would like to talk to Tracey about consulting work or speaking engagements, please use the Contact Us form.