Algorithmic Gender Bias in the Engineering Hiring Systems; Gender Bias in the Engineering Hiring System 19 views

Gender bias in the engineering hiring system continues to be a problem both technically and sociotechnically, with vague problem sources and limited available solutions. My capstone project report aims to provide a framework for a potential solution to this problem. This framework will serve as a starting point for bias evaluation tools in this field and will provide users with a better understanding of its causes. My STS research paper explores the main cause of this problem, specifically in the engineering hiring system. Having a better understanding of where bias is introduced into the hiring system will make it easier for developers to develop solutions to mitigate it. These two projects are connected because they investigate the problem and its solution. The combination of these works is essential for fully understanding how gender bias in the engineering hiring system is reinforced and how it can be mitigated. My capstone project focused on developing a standardized bias evaluation framework for engineering hiring systems through the proposed design of datasets, features, models, and workforce environments. My project addressed the problem of gender bias in hiring by recommending guidelines and procedures for each major step in the hiring system pipeline to help effectively mitigate gender bias. A major contribution of this framework is its inclusion of both technical and sociotechnical considerations. My project concluded that the use of this framework could improve both the technical and sociotechnical aspects of addressing gender bias in engineering hiring systems. From a technical viewpoint, hiring systems will be more thoroughly evaluated, purposeful, and transparent to the public. From a sociotechnical perspective, workplace culture could shift towards prioritizing honesty and fairness. Workers will also be better trained in bias mitigation and have a better understanding of the problem. My research paper investigated how hiring practices are perpetuating gender bias in the engineering workforce. This was deemed an essential problem because, beyond the ethical implications, this act violates the terms of the Civil Rights Act of 1964. To address this question, a case study was done on two major technology corporations, both of which were analyzed and compared through the following topics: DEI practices, hiring practices, and AI tools This case study concluded that both corporations persisted in practices that reinforced gender bias. Specifically, DEI practices were based on political influence, hiring practices reported for discriminatory behavior, and AI tools were reinforcing these measures. These conclusions were found through the analysis of the case study and application of the SCOT framework. Overall, this shows that the problem is still ongoing and needs to be addressed through mitigation methods such as DEI programs, bias training, oversight, and healthy work environments.

BS (Bachelor of Science)

gender; bias; hiring

School of Engineering and Applied Science Bachelor of Science in Computer Science Technical Advisor: Briana Morrison STS Advisor: Pedro Francisco

English

All rights reserved by the author (no additional license for public reuse)

2026-05-08