A Measure of Explanatory Effectiveness

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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In most conversations about explanation and AI, the recipient of the explanation (the explainee) is suspiciously absent, despite the problem being ultimately communicative in nature. We pose the problem 'explaining AI systems' in terms of a two-player cooperative game in which each agent seeks to maximise our proposed measure of explanatory effectiveness. This measure serves as a foundation for the automated assessment of explanations, in terms of the effects that any given action in the game has on the internal state of the explainee.
Original languageEnglish
Title of host publicationThe 1st International Workshop on Trusted Automated Decision-Making (TADM) co-located with ETAPS 2021
Publication statusPublished - 2021


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