DAISY is a structured form tool that generates more complete AI disclosure statements for research papers without reducing author comfort levels.
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Interval counterfactual explanations outperform point counterfactuals and feature importance scores in boosting model understanding and demonstrated trust according to a within-subjects user study.
Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather than post-hoc explanations.
citing papers explorer
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AI Disclosure with DAISY
DAISY is a structured form tool that generates more complete AI disclosure statements for research papers without reducing author comfort levels.
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Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
Interval counterfactual explanations outperform point counterfactuals and feature importance scores in boosting model understanding and demonstrated trust according to a within-subjects user study.
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Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Current XAI methods for DNNs and LLMs rest on paradoxes and false assumptions that demand a paradigm shift to verification protocols, scientific foundations, context-aware design, and faithful model analysis rather than post-hoc explanations.