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This doctoral research integrates Multi-Agent Systems (MAS) and reinforcement learning concepts into XAI evaluation and aims to systematically improve the robustness and clarity of AI explanations in complex, multi-stakeholder environments.
This work introduces XstrAI, an audience-aware multi-agent framework that treats local explanations as fixed evidence and structures how it is communicated to each target reader, and evaluates XstrAI on diabetes and stroke risk prediction against 11 baselines.
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