# Investigating Bias in Image Classification using Model Explanations

Source: [Investigating Bias in Image Classification using Model Explanations](<https://www.sota2.com/research/paper/68fa68dd1e209131cea1ec85>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:47:52.778Z

Authors: Schrasing Tong, Lalana Kagal (1) \_\_INSTITUTION\_2\_\_ Massachusetts Institute of Technology)

Published: 2020-12-10T05:27:49.000Z

## Abstract

We evaluated whether model explanations could efficiently detect bias in image classification by highlighting discriminating features, thereby removing the reliance on sensitive attributes for fairness calculations. To this end, we formulated important characteristics for bias detection and observed how explanations change as the degree of bias in models change. The paper identifies strengths and best practices for detecting bias using explanations, as well as three main weaknesses: explanations poorly estimate the degree of bias, could potentially introduce additional bias into the analysis, and are sometimes inefficient in terms of human effort involved.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2012.05463>)

[PDF](<https://arxiv.org/pdf/2012.05463.pdf>)