Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration

• URL: http://arxiv.org/abs/2304.14130v1
• Date: Thu, 27 Apr 2023 12:24:33 GMT
• Title: Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration
• Authors: Ioannis Papantonis, Vaishak Belle
• Abstract summary: We conduct an empirical study to identify how uncertainty estimates and model explanations affect users' reliance, understanding, and trust towards a model. We also discuss how the latter may distort the outcome of an analysis based on agreement and switching percentages.
• Score: 12.47276164048813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI and ML models have already found many applications in critical domains, such as healthcare and criminal justice. However, fully automating such high-stakes applications can raise ethical or fairness concerns. Instead, in such cases, humans should be assisted by automated systems so that the two parties reach a joint decision, stemming out of their interaction. In this work we conduct an empirical study to identify how uncertainty estimates and model explanations affect users' reliance, understanding, and trust towards a model, looking for potential benefits of bringing the two together. Moreover, we seek to assess how users' behaviour is affected by their own self-confidence in their abilities to perform a certain task, while we also discuss how the latter may distort the outcome of an analysis based on agreement and switching percentages.

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