MEA: A Reward-Driven Multi-Agent System for Faithful Model Explanations

Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their predictions. While post-hoc explanation methods offer a lens into this model behavior, wielding them…

Yuyang Cheng, Raghav Kaushik Ravi, Srivarshinee Sridhar, Sriparna Saha, Akash Ghosh, Chirag Agarwal·arXiv — cs.AI

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Key Takeaways

01.Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their predictions
02.While post-hoc explanation methods offer a lens into this model behavior, wielding them…

This material is provided as an extended analytical summary.

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