A Senior Capstone Experience by Tegan A. McBride ’22
Submitted to the Departments of Computer Science and Anthropology & Archeology
Advised by Dr. Kyle Wilson and Dr. Aaron Lampman
Contributor Biography: Tegan McBride recently graduated with the class of 2022 and has earned her Bachelor of Arts in Computer Science and Anthropology. During Commencement weekend, not only did she receive The Anthropology Award and George Washington Medal, but she also won the Holstein Prize for Ethics to recognize her culminating Senior Capstone Experience, The Innate Biases Involved in Interviews Conducted by Artificial Intelligence. Additionally, she received departmental honors from both of her majors for her SCE. Outside of her passion for illuminating algorithmic injustice, Tegan served as the President for the Douglass Cater Society of Junior Fellows and the President for Lambda Alpha, the Anthropology Honor’s Society. In her leisure time, she sings with WACappella, trains clients at Aquafit, and rides motorcycles. Her post-graduate plans are to continue advocating for algorithmic reform while engaging in her various hobbies.
Description: Our world’s most influential companies conduct interviews led by artificial intelligence. Because their power is so significant, multidisciplinary testing and well-rounded perspectives should be considered when designing and implementing this software. The purpose of my study is to assess the factors that may cause bias to be programmed into artificial intelligence interviewing software. To do this, I conducted interviews with companies that make this software and other people impacted by this technology, reviewed contemporary literature, reviewed diverse media sources, reviewed algorithmic audits from multiple disciplines, and examined previous uses of similar technology. This research is significant because not only is this a new and upcoming reality for most applicants, but also because we want our society to accept and promote diversity and inclusion in the workplace.
Read Teagan’s SCE below: