A Bayesian Account of Measures of Interpretability in Human-AI Interaction

• URL: http://arxiv.org/abs/2011.10920v1
• Date: Sun, 22 Nov 2020 03:28:28 GMT
• Title: A Bayesian Account of Measures of Interpretability in Human-AI Interaction
• Authors: Sarath Sreedharan, Anagha Kulkarni, Tathagata Chakraborti, David E. Smith and Subbarao Kambhampati
• Abstract summary: Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. We propose a revised model where all these behaviors can be meaningfully modeled together. We will highlight interesting consequences of this unified model and motivate, through results of a user study.
• Score: 34.99424576619341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing approaches for the design of interpretable agent behavior consider different measures of interpretability in isolation. In this paper we posit that, in the design and deployment of human-aware agents in the real world, notions of interpretability are just some among many considerations; and the techniques developed in isolation lack two key properties to be useful when considered together: they need to be able to 1) deal with their mutually competing properties; and 2) an open world where the human is not just there to interpret behavior in one specific form. To this end, we consider three well-known instances of interpretable behavior studied in existing literature -- namely, explicability, legibility, and predictability -- and propose a revised model where all these behaviors can be meaningfully modeled together. We will highlight interesting consequences of this unified model and motivate, through results of a user study, why this revision is necessary.

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