Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
This work is significant for researchers and practitioners in fair and interpretable AI, as it tackles the persistent problem of bias in image classification models, specifically within the CBM framework.
This paper addresses the issue of bias in Concept Bottleneck Models (CBMs) for image classification, where CBMs, despite their interpretability, have shown only marginal reductions in gender bias. The authors propose three techniques—a top-k concept filter, removal of biased concepts, and adversarial debiasing—to mitigate this bias, achieving improved fairness-performance tradeoffs compared to prior work.
Ensuring fairness in image classification prevents models from perpetuating and amplifying bias. Concept bottleneck models (CBMs) map images to high-level, human-interpretable concepts before making predictions via a sparse, one-layer classifier. This structure enhances interpretability and, in theory, supports fairness by masking sensitive attribute proxies such as facial features. However, CBM concepts have been known to leak information unrelated to concept semantics and early results reveal only marginal reductions in gender bias on datasets like ImSitu. We propose three bias mitigation techniques to improve fairness in CBMs: 1. Decreasing information leakage using a top-k concept filter, 2. Removing biased concepts, and 3. Adversarial debiasing. Our results outperform prior work in terms of fairness-performance tradeoffs, indicating that our debiased CBM provides a significant step towards fair and interpretable image classification.