Can AI Solve the Diversity Problem in the Tech Industry? Mitigating Noise and Bias in Employment Decision-Making

42 Pages Posted: 27 Mar 2019

Date Written: February 28, 2019

Abstract

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 2016 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 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

Suggested Citation

Houser, Kimberly, Can AI Solve the Diversity Problem in the Tech Industry? Mitigating Noise and Bias in Employment Decision-Making (February 28, 2019). 22 Stanford Technology Law Review ___ (Forthcoming). Available at SSRN: https://ssrn.com/abstract=3344751

Kimberly Houser (Contact Author)

Oklahoma State University ( email )

335 Spears College of Business
Stillwater, OK 74074
United States
+1(405)744-9430 (Phone)

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