#interpretable-ai3 articles

3 articles

AIBullisharXiv – CS AI · 4h ago4

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Interpretable Debiasing of Vision-Language Models for Social Fairness

Researchers have developed DeBiasLens, a new framework that uses sparse autoencoders to identify and deactivate social bias neurons in Vision-Language models without degrading their performance. The model-agnostic approach addresses concerns about unintended social bias in VLMs by making the debiasing process interpretable and targeting internal model dynamics rather than surface-level fixes.

AIBullisharXiv – CS AI · 4h ago5

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VISTA: Knowledge-Driven Vessel Trajectory Imputation with Repair Provenance

Researchers introduce VISTA, a framework for vessel trajectory imputation that uses knowledge-driven LLM reasoning to repair incomplete maritime tracking data. The system provides 'repair provenance' - documented reasoning behind data repairs - achieving 5-91% accuracy improvements over existing methods while reducing inference time by 51-93%.

AINeutralarXiv – CS AI · 4h ago0

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Hierarchical Concept-based Interpretable Models

Researchers introduce Hierarchical Concept Embedding Models (HiCEMs), a new approach to make deep neural networks more interpretable by modeling relationships between concepts in hierarchical structures. The method includes Concept Splitting to automatically discover fine-grained sub-concepts without additional annotations, reducing the burden of manual labeling while improving model accuracy and interpretability.