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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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