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

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  • Illingworth, A. James

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.

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  • Illingworth, A. James, 2015. "Big Data in I-O Psychology: Privacy Considerations and Discriminatory Algorithms," Industrial and Organizational Psychology, Cambridge University Press, vol. 8(4), pages 567-575, December.
    • Handle: RePEc:cup:inorps:v:8:y:2015:i:04:p:567-575_00
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    Cited by:

    1. Calvard, Thomas Stephen & Jeske, Debora, 2018. "Developing human resource data risk management in the age of big data," International Journal of Information Management, Elsevier, vol. 43(C), pages 159-164.

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