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A conversation about implicit bias

Article type: Research Article

Authors: Golbeck, Amanda L.a; * | Ash, Arleneb | Gray, Maryc | Gumpertz, Marciad | Jewell, Nicholas P.e | Kettenring, Jon R.f | Singer, Judith D.g | Gel, Yulia R.h

Affiliations: [a] Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA | [b] Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worchester, MA, USA | [c] Department of Mathematics and Statistics, American University, Washington, DC, USA | [d] Office of the Provost, North Carolina State University, Raleigh, NC, USA | [e] Department of Biostatistics, School of Public Health, University of California - Berkeley, Berkeley, CA, USA | [f] Research Institute for Scientists Emeriti, Drew University, Madison, NJ, USA | [g] Office of the Provost, Harvard University, Cambridge, MA, USA | [h] Department of Mathematical Sciences, University of Texas - Dallas, Richardson, TX, USA

Correspondence: [*] Corresponding author: Amanda L. Golbeck, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, 4301 West Markham, #820, Little Rock, AR 72201, USA. Tel.: +1 501 526 6611; E-mail:[email protected]

Keywords: Stereotypes, leadership, awards, women, minorities

DOI: 10.3233/SJI-161024

Journal: Statistical Journal of the IAOS, vol. 32, no. 4, pp. 739-755, 2016

Published: 15 November 2016
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Abstract

Explicit bias reflects our perceptions at a conscious level. In contrast, implicit bias is unintentional and operates at a level below our conscious awareness. Implicit stereotypes shaping implicit biases are widely studied in criminal justice, medicine, CEO selection at Fortune 500 companies, etc. However, the problem of unconscious bias remains. E.g., while women constitute an increasing proportion of all STEM undergraduates, they still make up only a small proportion of faculty members at research universities, and they are substantially under-represented in organizational leadership and as recipients of professional awards and prizes. Can we afford to have unintentional perceptions continue to hinder the success and advancement of women and other underrepresented groups? Can we afford to continue to underuse human capital in science? This session at the 2015 Joint Statistical Meetings (JSM) aimed to illuminate what statisticians need to know and do to break the glass ceiling of implicit bias.

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