Data & Civil Rights: Employment Primer

Data & Civil Rights Conference, October 2014

9 Pages Posted: 24 Dec 2014 Last revised: 9 Mar 2015

See all articles by Alex Rosenblat

Alex Rosenblat

Data & Society Research Institute

Kate Wikelius

The Leadership Conference on Civil and Human Rights

Danah Boyd

Data & Society Research Institute; Microsoft Research

Seeta Peña Gangadharan

New America Foundation - Open Technology Institute

Corrine Yu

The Leadership Conference on Civil and Human Rights

Date Written: October 30, 2014

Abstract

Employees and prospective employees produce more data than ever - in the workplace, on social media, and beyond. Employers and the third party companies that assist them increasingly apply analytical tools to these various data streams to measure factors that influence employee performance, attrition rates, and workplace profitability. While some of the data - such as past performance - are unquestionably relevant to such analysis, other data that produces strong correlations to performance are more surprising. For instance, Evolv, a recruiting software company, analyzed 3 million data points about 30,000 hourly employees and identified that those who installed newer browsers, like Chrome or Firefox, onto their computers stay at their jobs 15% longer than those who use default browsers that come pre-installed on their computers, like Safari for Macs. Job candidates may rightly worry that they will be excluded from or included in job opportunities based on data that seem arbitrary and are outside their field of vision. For example, a job candidate’s resume could be excluded from a talent pool because of her online browsing habits, but she is unlikely to find that out directly. The complexity of hiring algorithms which fold all kinds of data into scoring systems make it difficult to detect and therefore challenge hiring decisions, even when outputs appear to disadvantage particular groups within a protected class. When hiring algorithms weigh many factors to reach an unexplained decision, job applicants and outside observers are unable to detect and challenge factors that may have a disparate impact on protected groups.

Keywords: employment data, hiring algorithms, big data, disparate impact, discrimination, data-driven, scoring, labor, workforce

Suggested Citation

Rosenblat, Alex and Wikelius, Kate and Boyd, Danah and Gangadharan, Seeta Peña and Yu, Corrine, Data & Civil Rights: Employment Primer (October 30, 2014). Data & Civil Rights Conference, October 2014. Available at SSRN: https://ssrn.com/abstract=2541512 or http://dx.doi.org/10.2139/ssrn.2541512

Alex Rosenblat

Data & Society Research Institute ( email )

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

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

Kate Wikelius

The Leadership Conference on Civil and Human Rights ( email )

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/

Seeta Peña Gangadharan

New America Foundation - Open Technology Institute ( email )

1899 L St., N.W., Suite 400
Washington, DC 20036
United States

Corrine Yu

The Leadership Conference on Civil and Human Rights ( email )

1629 K Street NW
10th Floor
Washington, DC 20006
United States

HOME PAGE: http://www.civilrights.org/

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