Networked Employment Discrimination

Open Society Foundations' Future of Work Commissioned Research Papers 2014

17 Pages Posted: 31 Dec 2014

See all articles by Alex Rosenblat

Alex Rosenblat

Data & Society Research Institute

Tamara Kneese

University of San Francisco; Data & Society Research Institute

Danah Boyd

Data & Society Research Institute; Microsoft Research

Date Written: October 08, 2014

Abstract

Employers often struggle to assess qualified applicants, particularly in contexts where they receive hundreds of applications for job openings. In an effort to increase efficiency and improve the process, many have begun employing new tools to sift through these applications, looking for signals that a candidate is “the best fit.” Some companies use tools that offer algorithmic assessments of workforce data to identify the variables that lead to stronger employee performance, or to high employee attrition rates, while others turn to third party ranking services to identify the top applicants in a labor pool. Still others eschew automated systems, but rely heavily on publicly available data to assess candidates beyond their applications. For example, some HR managers turn to LinkedIn to determine if a candidate knows other employees or to identify additional information about them or their networks. Although most companies do not intentionally engage in discriminatory hiring practices (particularly on the basis of protected classes), their reliance on automated systems, algorithms, and existing networks systematically benefits some at the expense of others, often without employers even recognizing the biases of such mechanisms. The intersection of hiring practices and the Big Data phenomenon has not produced inherently new challenges. While this paper addresses issues of privacy, fairness, transparency, accuracy, and inequality under the rubric of discrimination, it does not pivot solely around the legal definitions of discrimination under current federal anti-discrimination law. Rather, it describes a number of areas where issues of inherent bias intersect with, or come into conflict with, socio-cultural notions of fairness.

Keywords: scoring society, employment algorithms, fairness, big data

Suggested Citation

Rosenblat, Alex and Kneese, Tamara and Boyd, Danah, Networked Employment Discrimination (October 08, 2014). Open Society Foundations' Future of Work Commissioned Research Papers 2014. Available at SSRN: https://ssrn.com/abstract=2543507 or http://dx.doi.org/10.2139/ssrn.2543507

Alex Rosenblat

Data & Society Research Institute ( email )

36 West 20th Street
New York,, NY
United States

HOME PAGE: http://www.datasociety.net

Tamara Kneese

University of San Francisco ( email )

2130 Fulton Street
San Francisco, CA 94117
United States

Data & Society Research Institute ( email )

36 West 20th Street
11th Floor
New York,, NY 10011
United States

Danah Boyd (Contact Author)

Data & Society Research Institute ( email )

36 West 20th Street
11th Floor
New York,, NY 10011
United States

HOME PAGE: http://www.datasociety.net

Microsoft Research ( email )

One Memorial Drive, 12th Floor
Cambridge, MA 02142
United States

HOME PAGE: http://research.microsoft.com/

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