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Most GNN explainers fail to detect indirect influences or alternative implementations of the same logic, even on seemingly robust benchmarks.
Humans make better decisions when AI selectively reveals information, but only if the AI learns *when* to reveal it, outperforming both full disclosure and no disclosure.
CBMs can significantly boost human-AI collaboration accuracy, but only under the right conditions, highlighting the delicate balance between interpretability and trust.
