Rapid trust calibration through interpretable and uncertainty-aware AI

Tomsett, Richard, Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057, Braines, Dave, Cerutti, Frederico, Chakraborty, Supriyo, Srivastava, Mani, Pearson, Gavin and Kaplan, Lance 2020. Rapid trust calibration through interpretable and uncertainty-aware AI. Patterns 1 (4) 10.1016/j.patter.2020.100049

Full text not available from this repository.

Abstract

Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations. These include having to deal with limited, low-quality data, which inevitably compromises AI performance. We suggest that these problems can be mitigated by taking steps that allow rapid trust calibration so that decision makers understand the AI system's limitations and likely failures and can calibrate their trust in its outputs appropriately. We propose that AI services can achieve this by being both interpretable and uncertainty-aware. Creating such AI systems poses various technical and human factors challenges. We review these challenges and recommend directions for future research.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Last Modified:	27 Jun 2023 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/160464

Citation Data

Cited 76 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item