Keeping Real World Bias Out of Artificial Intelligence ?Examination of Coder Bias in Data Science Recruitment Solutions?

Yvette Burton (yb2434@columbia.edu)
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Yvette Burton: Columbia University School of Professional Studies

No 9110624, Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences

Abstract: Research Question and Objectives: Is there subtle gender bias in the way companies word and code job listings in such fields as engineering and programming? Although the Civil Rights Act effectively bans companies from explicitly requesting workers of a particular gender, the language in these listings may discourage many women from applying.The objectives of the research are to create to foundational constructs leaders can use to address the growing employee competency and business performance gaps created by the impact of lack of gender diversity among data scientist roles, and siloes across enterprise talent strategies. These two objectives include: Integrated Data Scientist and HCM Leadership Development Strategies and AI Leadership Assessment and Development w/ Risk Audits.

Keywords: Coding Bias; Artificial Intelligence; Data Scientists; Leadership Development; Business Performance; Digital Workforce Solutions; Behavioral Analytics; Twenty-First Century Skills Gaps; Human Capital Management; STEM; Enterprise Risk Management. (search for similar items in EconPapers)
JEL-codes: C89 D81 J24 (search for similar items in EconPapers)
Pages: 1 page
Date: 2019-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hrm and nep-pay
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Published in Proceedings of the Proceedings of the 46th International Academic Conference, Rome, Jul 2019, pages 98-98

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