Data Analytics and the Erosion of the Work/Nonwork Divide

• Date: 01 September 2019
• DOI: http://doi.org/10.1111/ablj.12146
• Author: Leora Eisenstadt
• Published date: 01 September 2019

American Business Law Journal

Volume 56, Issue 3, 445–506, Fall 2019

Data Analytics and the Erosion of the

Work/Nonwork Divide

Leora Eisenstadt*

Numerous statutes and common law doctrines conceive of a dividing line between

work time and nonwork 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 nonwork in dramatically greater and more troubling ways than ever before.

The emergence of programs allowing employers to track, predict, rely upon, and

possibly control nonwork activities, views, preferences, and emotions represents a

major blurring of the line between work and nonwork. This article contends that

these advances in data analytics suggest a need to reexamine the notion of work

versus nonwork time and to question whether existing protections adequately con-

sider a world in which these lines are 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 accept-

able 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 choose our own course rather than allowin g the technology

to be the guide.

*Assistant Professor, Department of Legal Studies, Fox School of Business, Temple University;

B.A., Yale University; J.D., New York University School of Law, L.L.M, Temple University

Beasley School of Law. This article benefitted tremendously from discussions at the Sixteenth

Huber Hurst Research Seminar at the University of Florida, the XVI International Conference

in commemoration of Prof. Marco Biagi in Modena, Italy, the 2018 Academy of Legal Studies

in Business Annual Conference, and from in-depth discussions with the Department of Busi-

ness Law and Ethics at Indiana University’s Kelley School of Business. Special thanks to

Michael Gangnath for introducing me to the thorny issue of data analytics in the workplace, to

Charlotte Alexander for talking through the idea for this article with me in its early stages, and

to Jamie Prenkert and Anjanette Raymond for their important insights in its final stages.

©2019 The Author

American Business Law Journal ©2019 Academy of Legal Studies in Business

445

INTRODUCTION

The application of predictive analytics to people’s careers… is enormously

challenging, not to mention ethically fraught. And it can’t help but feel a lit-

tle creepy. It requires the creation of a vastly larger box score of human per-

formance than one would ever encounter in the sports pages, or that has

ever been dreamed up before. To some degree, the endeavor touches on the

deepest of human mysteries: how we grow, whether we flourish, what we

become. Most companies are just beginning to explore the possibilities. But

make no mistake: during the next five to 10 years, new models will be cre-

ated, and new experiments run, on a very large scale.

1

Imagine a workplace in which the employer had access to millions of bits

of data about its employees—their off-duty hobbies, consumer prefer-

ences, and political views; the fact that they are contemplating becoming

pregnant or concerned about developing diabetes; their heart rates,

amounts of physical activity, and sleep patterns; even their state of mind

when arriving to and leaving from work. What if that employer could

use all of that information to make decisions about the design of the

workplace, create teams and identify potential leaders, determine insur-

ance rates for workers, and offer professional development opportuni-

ties? This, unbeknownst to many of us, is the modern-day workplace.

Because of our reliance on the Internet, our addiction to social media,

and our general disregard for privacy concerns, most of us have left

enormous data trails that employers are now beginning to access to cre-

ate the most efficient workplaces possible.

2

In so doing, however, they

have extended their reach beyond the information gleaned from

workers at work and have begun to collect and use data concerning

employees’ personal lives. While this data may be useful to employers,

the collection and use of such information constitutes a new overreach

that will lead to a substantial erosion of the work/nonwork divide and

result in problematic outcomes for employers and employees alike.

Big data offers numerous benefits to society. For example, the abil-

ity to aggregate and analyze massive quantities of data makes it

1

Don Peck, They’re Watching You at Work,THE ATLANTIC, Dec. 2013, https://www.theatlantic.

com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/.

2

The Enormous Data Trail We Generate Throughout the Day, BBVA (Aug. 24, 2016), https://www.

bbva.com/en/the-enormous-data-trail-we-generate-throughout-the-day/; Manoush Zomorodi,

DoYouKnowHowMuchPrivateInformationYouGiveAwayEveryDay?,T

IME (Mar. 29, 2017),

http://time.com/4673602/terms-service-privacy-security/.

446 Vol. 56 / American Business Law Journal

possible to track previously unseen trends in human health,

3

pro-

vides new information about the legal system and the tendencies of

courts,

4

offers more accurate predictions of weather patterns,

5

and

allows for innovative responses to humanitarian crises.

6

The most

obvious and prevalent uses of big data continue to arise in the busi-

ness world where the ability to analyze extremely large data sets

allows companies to track and respond to customer preferences and

to identify, rank, and rate consumers and workers alike.

7

These uses

can be both beneficial to society and extremely troubling because of

privacy concerns, the fear that inaccurate information will remain

permanently trapped in the system, and the potential for unseen

biases to skew the data.

8

The use of big data by employers is of particular concern from both

ethical and legal perspectives. Remarkably, there is a dearth of legal reg-

ulation specifically governing this type of data gathering and use. In the

last several years, scholars have begun to focus on the ethical implications

of data analytics at work, the lack of transparency in their use, the pri-

vacy issues created by these technological advances, and the ways in

which existing discrimination law is and is not implicated.

9

This research

breaks new ground in the field by examining the impact of data analytics

on the divide between work and nonwork spheres, and considers the

3

See,e.g., Wullianallur Raghupathi & Viju Raghupathi, Big Data Analytics in Healthcare:Prom-

ise and Potential,2H

EALTH INFO.SCI.SYS. (2014), https://link.springer.com/content/pdf/10.

1186%2F2047-2501-2-3.pdf.

4

See,e.g., STANFORD LAW SCHOOL SECURITIES CLASS ACTION CLEARINGHOUSE, https://law.

stanford.edu/securities-class-action-clearinghouse-scac/ (last visited June 30, 2019).

5

Seeta Pen

˜a Gangadharan, How Can Big Data Be Used for Social Good?,THE GUARDIAN (May

30, 2013), https://www.theguardian.com/sustainable-business/how-can-big-data-social-good.

6

Id.

7

See Frank Pasquale, Digital Star Chamber,AEON MAG. (Aug. 18, 2015), https://aeon.co/essays/

judge-jury-and-executioner-the-unaccountable-algorithm.

8

Id.

9

See,e.g., Matthew T. Bodie et al., The Law and Policy of People Analytics,88U.COLO.L.REV.

961, 987–1002 (2017); Pauline T. Kim, Data-Driven Discrimination at Work,58W

M.&MARY

L. REV. 857, 865–920 (2017); Kate Crawford & Jason Schultz, Big Data and Due Process:

Toward a Framework to Redress Predictive Privacy Harms, 55 B.C. L. REV. 93, 94 (2014); Solon

Barocas & Andrew D. Selbst, Big Data’s Disparate Impact, 104 CALIF.L.REV. 671, 674 (2016).

2019 / Data Analytics and the Erosion of the Work/Nonwork Divide 447