Date of Submission

Spring 2022

Academic Program

Computer Science; Mathematics

Project Advisor 1

Robert McGrail

Project Advisor 2

Caitlin Leverson

Abstract/Artist's Statement

Artificial intelligence and machine learning systems are becoming ever more prevalent; at every turn these systems are asked to make decisions that have lasting impacts on peoples’ lives. It is becoming increasingly important that we ensure these systems are making fair and equitable decisions. For decades we have been aware of biased and unfair decision making in many sectors of society. In recent years a growing body of evidence suggests these biases are being captured in data that are then used to build artificial intelligence and machine learning systems, which themselves perpetuate these biases. The question is then, can we detect these biases in the data before it is used to create these systems? In this paper we will be exploring the feasibility and effectiveness of using a technique from topological data analysis to detect unfair bias in a criminal sentencing dataset.

Open Access Agreement

Open Access

Creative Commons License

Creative Commons License
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