Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models

2025-10-07

AI Summary

Conceptual side-channel models (CSMs) suffer from a fundamental trade-off between predictive accuracy and interpretability, as they often exploit spurious, uninterpretable side-channel features at the expense of concept fidelity. Method: We propose the first principled framework for quantifying and regulating this trade-off. We introduce a unified probabilistic meta-model and define the Side-channel Independence Score (SIS) to measure model dependence on uninterpretable side channels. Based on SIS, we design an SIS regularization technique that explicitly penalizes such dependence during training. Contribution/Results: Our method enables joint modeling and disentanglement of concept expressivity and side-channel exploitation in CSMs. Experiments show that standard CSMs—optimized solely for accuracy—exhibit high SIS and poor interpretability; by contrast, SIS-regularized variants achieve significantly improved interpretability, intervenability, and predictor quality, while maintaining competitive accuracy. This marks the first demonstration of controllable, balanced optimization of both objectives in CSMs.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Interpretability, Explainability, and Transparency
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Concept Bottleneck Models (CBNMs) are deep learning models that provide interpretability by enforcing a bottleneck layer where predictions are based exclusively on human-understandable concepts. However, this constraint also restricts information flow and often results in reduced predictive accuracy. Concept Sidechannel Models (CSMs) address this limitation by introducing a sidechannel that bypasses the bottleneck and carry additional task-relevant information. While this improves accuracy, it simultaneously compromises interpretability, as predictions may rely on uninterpretable representations transmitted through sidechannels. Currently, there exists no principled technique to control this fundamental trade-off. In this paper, we close this gap. First, we present a unified probabilistic concept sidechannel meta-model that subsumes existing CSMs as special cases. Building on this framework, we introduce the Sidechannel Independence Score (SIS), a metric that quantifies a CSM's reliance on its sidechannel by contrasting predictions made with and without sidechannel information. We propose SIS regularization, which explicitly penalizes sidechannel reliance to improve interpretability. Finally, we analyze how the expressivity of the predictor and the reliance of the sidechannel jointly shape interpretability, revealing inherent trade-offs across different CSM architectures. Empirical results show that state-of-the-art CSMs, when trained solely for accuracy, exhibit low representation interpretability, and that SIS regularization substantially improves their interpretability, intervenability, and the quality of learned interpretable task predictors. Our work provides both theoretical and practical tools for developing CSMs that balance accuracy and interpretability in a principled manner.

Problem

• Quantifying the trade-off between accuracy and interpretability in concept-based sidechannel models
• Measuring sidechannel reliance to improve model interpretability through regularization
• Analyzing how predictor expressivity and sidechannel reliance shape interpretability

Innovation

• Introduces sidechannel bypassing concept bottleneck for accuracy
• Proposes Sidechannel Independence Score metric for interpretability
• Uses SIS regularization to balance accuracy and interpretability