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Found 2 papers, 1 papers with code
Why Don't You Do Something About It? Outlining Connections between AI Explanations and User Actions
A core assumption of explainable AI systems is that explanations change what users know, thereby enabling them to act within their complex socio-technical environments.
Experiential Explanations for Reinforcement Learning
However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen.
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