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22 Stan. Tech. L. Rev. 290 (2019)
Can AI Solve the Diversity Problem in the Tech Industry: Mitigating Noise and Bias in Employment Decision-Making

handle is hein.journals/stantlr22 and id is 291 raw text is: 








  Can AI Solve the Diversity Problem in the

  Tech   Industry? Mitigating Noise and Bias in

           Employment Decision-Making


                      Kimberly A. Houser*


                    22 STAN. TECH. L. REV. 290 (2019)

                               ABSTRACT

        After thefirst diversity report was issued in 2014 revealing the dearth
ofwomen  in the tech industry, companies rushed to hire consultants to provide
unconscious bias training to their employees. Unfortunately, recent diversity
reports show no significant improvement, and, in fact, women lost ground
during some  of the years. According to a Human Capital Institute survey,
nearly 80%  of leaders were still using gut feeling and personal opinion to
make  decisions that affected talent-management practices. By incorporating
AI  into employment   decisions, we can mitigate  unconscious bias and
variability (noise) in human decision-making. While some  scholars have
warned  that using artificial intelligence (AI) in decision-making creates
discriminatory results, they downplay the reason for such occurrences-
humans.  The main concerns noted relate to the risk of reproducing bias in an
algorithmic outcome (garbage in, garbage out) and the inability to detect
bias due to the lack of understanding of the reason for the algorithmic
outcome  (black box problem). In this paper, I argue that responsible AI will
abate the problems caused by unconscious biases and noise in human decision-
making,  and in doing so increase the hiring, promotion, and retention of
women  in the tech industry. The new solutions to the garbage in, garbage out
and black box concerns will be explored. The question is not whetherAI should


     *  Kimberly A. Houser is an assistant professor at Oklahoma State University. The
author would like to thank the participants at the 2018 Law and Ethics of Big Data
Colloquium in Wellesley, Massachusetts, sponsored by Babson College, Virginia Tech,
Center for Business Intelligence and Analytics, Pamplin College of Business, and Indiana
University, Department of Legal Studies, for their helpful remarks. The author would also
like to additionally thank Angie Raymond, Ramesh Sharda, Griffin Pivateau, and Laurie
Swanson Oberhelman for their insightful comments and to Haley Amster, Justin Bryant,
Abigail Pace, Katherine Worden, Collin Hong, and Caroline Lebel for their thoughtful and
thorough editing, proof-reading, and cite-checking.

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