Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models

Researchers propose concept-based abductive and contrastive explanations that identify minimal sets of high-level concepts causally relevant to vision model predictions. The approach combines human-interpretable concept-based explanations with formal causal reasoning, enabling better understanding of both individual predictions and common model behaviors across image collections.

This research addresses a fundamental challenge in AI interpretability: explaining deep neural network decisions in ways that humans can understand and verify. Traditional concept-based explanations lack formal causal grounding, while formal abductive methods operate only on low-level pixel features. The proposed framework bridges this gap by establishing causal relationships between abstract, human-understandable concepts and model outputs through concept erasure procedures.

The work emerges from growing demands for transparency in AI systems, particularly as vision models are deployed in high-stakes applications like medical imaging, autonomous systems, and content moderation. Current explanation methods either sacrifice precision for interpretability or vice versa, limiting their practical utility. By computing minimal explanations—only the essential concepts needed to explain outcomes—the approach provides more efficient and trustworthy interpretations than existing alternatives.

For practitioners developing vision systems, these methods offer significant value in debugging model behavior, identifying spurious correlations, and understanding failure modes. The ability to explain common behaviors across image collections helps teams detect systematic biases or unexpected decision patterns at scale. This capability strengthens the argument for interpretable AI in regulated industries and increases user trust in automated systems.

The research demonstrates that formal rigor and human interpretability are not mutually exclusive. Future work may extend these concepts to other modalities and explore integration with active learning frameworks, where explanations guide data collection strategies. As regulatory pressure for AI transparency intensifies, concept-based causal explanations could become standard practice in model development and validation workflows.

• →Combines concept-based explanations with formal causal reasoning to identify minimal sets of concepts relevant to model predictions.
• →Uses concept erasure procedures to establish causal relationships between high-level concepts and vision model outputs.
• →Aggregates explanations across image collections to understand common model behaviors and detect systematic patterns.
• →Addresses the interpretability-precision tradeoff by providing both human-understandable and formally rigorous explanations.
• →Enables practical debugging and bias detection at scale for vision systems deployed in sensitive applications.