Can AI Solve the Diversity Problem in the Tech Industry? Mitigating Noise and Bias in Employment Decision-Making
Kimberly A. Houser, Can AI Solve the Diversity Problem in the Tech Industry? Mitigating Noise and Bias in Employment Decision-Making, 22 Stan. Tech. L. Rev. 290 (2019). By permission of the Board of Trustees of the Leland Stanford Junior University, from the Stanford Technology Law Review at 22 STA
64 Pages Posted: 12 Jul 2020
Date Written: February 28, 2019
After the first diversity report was issued in 2014 revealing the dearth of women 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 whether AI should be incorporated into decisions impacting employment, but rather why in 2019 are we still relying on faulty human decision-making.
Keywords: artificial intelligence, AI, diversity, decision-making, noise, unconscious bias, black box, tech industry, gender discrimination
JEL Classification: K2
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