Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

By Javier Chiyah-Garcia, David A. Robb, Xingkun Liu, Atanas Laskov, Pedro Patrón, Helen Wright Hastie. 2018

46 citations recorded by the source corpus.

As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Reframing Human-AI Collaboration for Generating Free-Text Explanations (2021, methodology): “Our interface is inspired by prior work from psychology and the social sciences (Leake, 1991; Gopnik, 1998; Lom-brozo, 2007; Zemla et al., 2017; Chiyah Garcia et al., 2018; Clinciu et al., 2021; Sulik et al., 2021).”
A Study of Automatic Metrics for the Evaluation of Natural Language Explanations (2021, background): “This was partly the case described in (Chiyah Garcia et al., 2018), but here the users were experts and preferred to be given all possible reasons but as precise and brief as possible.”
Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems (2020, background): “In contrast, studies that use proxy tasks evaluate how well users are able to simulate the model’s decisions [6, 13, 27, 34] or decision boundaries [29].”