research-article

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Authors:

Ana Marasović,

Yoav GoldbergAuthors Info & Claims

FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Pages 624 - 635

https://doi.org/10.1145/3442188.3445923

Published: 01 March 2021 Publication History

Abstract

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, interpersonal trust (i.e., trust between people) as defined by sociologists. This model rests on two key properties: the vulnerability of the user; and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI model is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (that detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.

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Xuan YSmall ESokol KHettiachchi DSanderson M(2025)Comprehension is a double-edged sword: Over-interpreting unspecified information in intelligible machine learning explanationsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2024.103376193(103376)Online publication date: Jan-2025
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Chiaburu THaußer FBießmann F(2024)Uncertainty in XAI: Human Perception and Modeling ApproachesMachine Learning and Knowledge Extraction10.3390/make60200556:2(1170-1192)Online publication date: 27-May-2024
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Peng WLee HLim S(2024)Leveraging Chatbots to Combat Health Misinformation for Older Adults: Participatory Design StudyJMIR Formative Research10.2196/607128(e60712)Online publication date: 11-Oct-2024
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Wang XZhang YZheng J(2024)Beyond the Value for AI Adopters: Analyzing the Impacts of Autonomous Vehicle Testing on Traffic Conditions in CaliforniaSSRN Electronic Journal10.2139/ssrn.4775378Online publication date: 2024
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Matzen LGastelum ZHowell BDivis KStites M(2024)Effects of machine learning errors on human decision-making: manipulations of model accuracy, error types, and error importanceCognitive Research: Principles and Implications10.1186/s41235-024-00586-29:1Online publication date: 26-Aug-2024
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Mahony SChen Q(2024)Concerns about the role of artificial intelligence in journalism, and media manipulationJournalism10.1177/14648849241263293Online publication date: 19-Jun-2024
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