Data-driven decision-making is now the norm in many workplaces. Executives collect and analyze information to inform hiring practices, promotions, and insurance premiums. However, Leora Eisenstadt warns that the kinds of data that employers can track should be safeguarded by law, to both protect employees’ privacy and limit employers’ liability.
Eisenstadt asserts that the current legal statutes do not provide enough protection to both employers and employees. By gathering data from nonwork activities, employers may be pushing this trend to new, more troubling places. In today’s environment of data concerns and privacy breaches, companies should be cautious of data mining that goes too far.
Leora F. Eisenstadt, Data Analytics and the Erosion of the Work/Non-Work Divide, 56 American Business Law Journal 445 (2019).
Numerous statutes and common law doctrines conceive of a dividing line between work time and non-work time and delineate the activities that must be compensated as work. While technological innovations and increasing desires for workplace flexibility have begun to erode this divide, it persists, in part, because of the ways in which the division protects employers and employees alike. Nonetheless, the explosion of data analytics programs that allow employers to monitor and rely upon a worker’s off-duty conduct will soon weaken the dividing line between work and non-work in dramatically greater and more troubling ways than ever before. Examples of these advances abound. Employers have begun to rely on algorithms that harvest massive quantities of data from employees’ social media and other online profiles and use this data to screen for the most productive teams and the best workers. Employers can now use data analytics to track and predict their employees’ family planning thoughts and healthcare concerns or use facial recognition technology and sentiment analysis to forecast employees’ emotional states. The emergence of these programs allowing employers to track, predict, rely upon, and possibly control non-work activities, views, preferences, and emotions represents a major blurring of the line between work and non-work. Data Analytics and the Erosion of the Work/Non-work Divide contends that these advances in predictive analytics suggest a need to re-examine the notion of work vs. non-work time and to question whether existing protections adequately consider a world in which these lines have been so significantly muddled. As a society, we need to acknowledge the implications of the availability of massive quantities of employees’ off-duty data and to decide whether and how to regulate its use by employers. Whether we, as a society, decide to allow market forces to dictate acceptable employer behavior, choose to regulate and restrict the use of off-duty data for adverse employment decisions, or find some middle ground that requires disclosure and consent, we should be choosing a course rather than allowing the technological innovations to be the guide.