arXiv AI
When Explanations Compete: Policy-Aware Selection Under Uncertainty
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arXiv:2606. 10347v1 Announce Type: new Abstract: Machine learning is increasingly used in critical domains, where both predictions and their associated confidence levels influence important decisions.
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Can Revealed Preferences Clarify LLM Alignment and Steering?
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Target-confidence Recourse Using tSeTlin machines: TRUST
arXiv:2606. 18832v1 Announce Type: cross Abstract: Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems.
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Target-confidence Recourse Using tSeTlin machines: TRUST
Counterfactual explanations are widely used to provide algorithmic recourse in high-stakes decision-making systems. Most existing methods seek the smallest change to an input that flips a model's decision.
arXiv Machine Learning
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Explainable Uncertainty Estimation for Reliable Medical AI
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