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Activity Number: 14
Type: Invited
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Caucus for Women in Statistics
Abstract #314415
Title: Implicit Bias: What Statisticians Need to Know and Do
Author(s): Nicholas P. Jewell* and Judith Singer* and Arlene Ash* and Jon Kettenring* and Marcia Gumpertz* and Mary W. Gray*
Companies: UC Berkeley and Harvard University and University of Massachusetts and Drew University and North Carolina State University and American University
Keywords: implicit bias ; discrimination ; underrepresented minorities ; leadership ; STEM ; legal statistics
Abstract:

Explicit bias reflects our perception at a conscious level. In contrast, implicit bias is unintentional and operates at a level below our conscious awareness. Implicit stereotypes shaping this hidden bias are widely studied in in criminal justice, medicine, CEO selection at Fortune 500 companies etc. Many concrete obstacles on the path to professional success of underrepresented minorities in STEM fields have been largely overcome. However, the problem of unconscious bias still remains one of the most challenging barriers. E.g., while women constitute an increasing proportion of all STEM undergraduates, they still make up only a very small proportion of faculty members at research universities. Their representation in organizational leadership positions, and among recipients of professional awards and prizes, still remains substantially low. Can we afford to have such 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 aims to illuminate what statisticians need to know and do to break this glass ceiling of implicit bias.


Authors who are presenting talks have a * after their name.

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