A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
Proceedings of the AAAI Conference on Artificial Intelligence , author=
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LLM narrative explanations of varying persuasiveness did not improve human decision accuracy over AI predictions alone but increased reliance on AI even when incorrect.
Reveal-to-Revise integrates cross-modal attention fusion, Grad-CAM++ attribution, and bias feedback in a conditional attention WGAN-GP to report high accuracy, F1, and fairness metrics on multimodal MNIST variants and toxic text tasks.
citing papers explorer
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
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Human Decision-Making with Persuasive and Narrative LLM Explanations
LLM narrative explanations of varying persuasiveness did not improve human decision accuracy over AI predictions alone but increased reliance on AI even when incorrect.
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Reveal-to-Revise: Explainable Bias-Aware Generative Modeling with Multimodal Attention
Reveal-to-Revise integrates cross-modal attention fusion, Grad-CAM++ attribution, and bias feedback in a conditional attention WGAN-GP to report high accuracy, F1, and fairness metrics on multimodal MNIST variants and toxic text tasks.