A Formal Methodological Framework for Auditing Robustness and Fidelity in Explai
arXiv:2604.22662v1 Announce Type: cross Abstract: Shapley values are a cornerstone of explainable AI, yet their proliferation into competing formulations has created a fragmented
arXiv:2607.09649v1 Announce Type: new Abstract: Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level
arXiv:2605.07002v1 Announce Type: new Abstract: A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation.
arXiv:2610.14356v1 Announce Type: new Abstract: AI-enabled systems must remain dependable when the conditions under which they operate change (e.g., due to input perturbations,
arXiv:2605.13046v1 Announce Type: new Abstract: Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical
arXiv:2605.16198v1 Announce Type: new Abstract: We examine one particular dimension of AI governance: how to monitor and audit AI-enabled products and services throughout the AI
arXiv:2604.21854v1 Announce Type: new Abstract: Artificial intelligence now decides who receives a loan, who is flagged for criminal investigation, and whether an autonomous
arXiv:2610.21581v1 Announce Type: new Abstract: Mathematical agents are commonly evaluated by answer accuracy or proof acceptance. We introduce ClaimScope, a 72-task benchmark for
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