DATA ANALYTICS AT WORK: A VIEW FROM ISRAEL ON EMPLOYEE PRIVACY AND EQUALITY IN THE AGE OF DATA-DRIVEN EMPLOYMENT MANAGEMENT

This article is concerned with the use of data analytics at work- algorithmic determinations concerning the management of workers, provided on the basis of large quantities of data about employees, or potential employees, collected, collated, mined, and classified from various sources. The article considers such practices as they pertain to two stages of employment management: the hiring phase and the on-the-job phase. This seems especially important today as a developing field of people analytics emerges by injecting data mining into human resource management, aiming to eschew gut feelings in favor of the more systemic and supposedly scientific data analysis as a tool to guide hiring, promotion, demotion, scheduling, wages, and discharge determinations,1 yet raising serious concerns about employee privacy and equality.2 As a comparative project, the article provides a view from Israel to show that on-the-ground practices of data analytics seem to be permeating the Israeli workforce. The Israeli experience shows that while on-the-books equality and privacy are fundamental values in Israeli employment law, in-action data analytics currently may pose unspoken risks to both.The article proceeds as follows. Part I provides an overview of the literature on data mining and people analytics at work and the privacy and equality concerns it raises. Part II provides evidence that people analytics is permeating the Israeli workforce. Part III explains the protections currently afforded to privacy and to equality in Israeli employment law, describing the relevant basic rights, statutes, cases and regulatory guidelines. It shows that despite important advances already made in protecting privacy at work and in prohibiting discrimination, data analytics may currently hold risks to both. Part IV concludes with some preliminary suggestions for thinking about privacy and equality in Israeli law in the era of data-driven employment management.