Big Data in I-O Psychology: Privacy Considerations and Discriminatory Algorithms

Industrial and Organizational PsychologyPublished 1 December 2015

A. James Illingworth

DOI10.1017/iop.2015.85

Source URL

Citations10

SJR quartileQ1

SJR score1.18

SNIP1.02

TL;DR

One of the first attempts to establish a formal set of recommendations for working with big data in ways that are consistent with I-O psychology's professional guidelines and ethics requirements is presented.

Abstract

The “big data” movement is forcing many fields to establish best practices for the collection, analysis, and application of big data, and the field of industrial–organizational (I-O) psychology is not exempt from this disruptive influence. Over the last several years, I-O scientists and practitioners have grappled with questions related to the definition, application, and interpretation of big data (e.g., Doverspike, 2013; Maurath, 2014; Morrison & Abraham, 2015; Poeppelman, Blacksmith, & Yang, 2013). The focal article by Guzzo, Fink, King, Tonidandel, and Landis (2015) continues this discussion and represents one of the first attempts to establish a formal set of recommendations for working with big data in ways that are consistent with I-O psychology's professional guidelines and ethics requirements.

Keywords

Social SciencesBusiness, Management and Accounting

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