A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
Towards a Rigorous Science of Inter- pretable Machine Learning, in: Proceedings of the ICML Workshop on Human Interpretability in Machine Learning, Sydney, Australia
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What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.