Understanding and mitigating bias in AI, an application to biometrics

Biometric systems, particularly those utilized in soft biometric analytics encompassing recognition and classification tasks, often demonstrate biases that disproportionately impact specific demographic groups, including gender and race. The mitigation of these biases is paramount to ensure fairness in algorithmic decision-making processes. However, prevailing bias mitigation techniques face constraints related to their limited generalizability, reliance on demographically annotated training data, and specific application constraints. Additionally, addressing bias typically involves a delicate balance between fairness and classification accuracy, where prioritizing fairness may result in diminished accuracy, especially for the most proficient demographic subgroups. To address this challenge, This dissertation investigates the presence of biases in soft biometric attribute classification algorithms and proposes innovative bias mitigation strategies aimed at enhancing fairness without compromising the overall performance of the system.

Author

• Upendran Nair, Anoop Krishnan

Identifier

hdl:10057/27838

OAI identifier oai:identifier

oai:soar.wichita.edu:10057/27838