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

arXiv: https://arxiv.org/abs/2011.10920 · HTML: https://mlsanity.com/paper/2011.10920/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)
- [Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction](https://mlsanity.com/paper/2105.01220/) (2021)
- [A Mental Model Based Framework of Trust](https://mlsanity.com/paper/2301.12569/) (2023)
- [Generating Active Explicable Plans in Human-Robot Teaming](https://mlsanity.com/paper/2109.08834/) (2021)

## Abstract (arXiv)

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.