Date of Award
Spring 5-31-2023
Document Type
Thesis (Undergraduate)
Department
Computer Science
First Advisor
Charles Palmer
Abstract
Artificial Intelligence (AI) models are increasingly used as predictive tools with real-world applications occurring in diverse fields ranging from the healthcare industry to the criminal justice system. While AI often offers efficient and relatively effective solutions, there are growing concerns regarding AI’s role in decision-making processes due to potential biases embedded in these models. In many cases, bias in AI models can produce unfair outcomes, perpetuate social inequities, and undermine the trustworthiness of AI systems. This thesis explores this problem and spotlights certain biased models that are currently utilized in real-world situations. One such example is a highly biased AI algorithm, COMPAS, that is used in the criminal justice system to assign recidivism scores to criminal defendants and impact their sentences. While there is, unfortunately, no current method to create entirely unbiased AI algorithms, this paper details techniques that have been applied to mitigate the presence of bias in AI models.
Recommended Citation
Martin, Julia L., "Unmasking Bias: Investigating Strategies for Minimizing Discrimination in AI Models" (2023). Computer Science Senior Theses. 6.
https://digitalcommons.dartmouth.edu/cs_senior_theses/6
