arXiv:2605.29836v1 Announce Type: new Abstract: Despite strong average-case performance, deep learning models often exhibit systematic errors on specific population groups, known as error slices. Identifying these groups and the root causes of their failures is critical for model debugging and bias mitigation. However, existing error Slice Discovery Methods (SDMs) typically generate explanations disconnected from the model's inference process, thus only approximating the underlying error source and may be inaccurate. We address this limitation by leveraging Concept Bottleneck Models (CBMs), wh
The increasing deployment of deep learning models across critical applications highlights the urgent need for robust debugging and bias mitigation techniques, which this research aims to address.
Improving the interpretability and reliability of AI models is crucial for their adoption in sensitive domains and for building trust, directly impacting their commercial and societal utility.
The proposed CB-SLICE method offers a more precise way to identify and understand systemic AI errors by connecting them directly to the model's inference process, moving beyond disconnected explanations.
- · AI developers and researchers
- · Industries deploying AI in critical systems (e.g., healthcare, finance)
- · AI ethics and safety organizations
- · Developers relying solely on black-box model development
- · Applications plagued by unexplainable systematic errors
- · Organizations facing regulatory scrutiny for biased AI
Increased understanding and control over AI model behavior, leading to more robust and trustworthy systems.
Faster debugging and validation cycles for AI models, accelerating their development and deployment in sensitive sectors.
Enhanced regulatory frameworks around AI interpretability and bias, driven by more effective detection and explanation capabilities.
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