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Title: What's Fair is Fair: Detecting and Mitigating Encoded Bias in Multimodal Models of Museum Visitor Attention
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Date Published:
Journal Name:
International Conference on Multimodal Interaction
Medium: X
Sponsoring Org:
National Science Foundation
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  1. Fair Learning To Rank (LTR) frameworks require demographic information; however, that information is often unavailable. Inference algorithms may infer the missing demographic information to supply to the fair LTR model. In this study, we analyze the effect of using a trained fair LTR model with uncertain demographic inferences. We show that inferred data results in varying levels of fairness and utility depending on inference accuracy. Specifically, less accurate inferred data adversely affects the rankings’ fairness, while more accurate inferred data creates fairer rankings. Therefore, we recommend that a careful evaluation of demographic inference algorithms before use is critical. 
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