(Re)Conceptualizing trustworthy AI: A foundation for change

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence Pub Date : 2025-02-22 DOI:10.1016/j.artint.2025.104309

Christopher D. Wirz , Julie L. Demuth , Ann Bostrom , Mariana G. Cains , Imme Ebert-Uphoff , David John Gagne II , Andrea Schumacher , Amy McGovern , Deianna Madlambayan

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Abstract

Developers and academics have grown increasingly interested in developing “trustworthy” artificial intelligence (AI). However, this aim is difficult to achieve in practice, especially given trust and trustworthiness are complex, multifaceted concepts that cannot be completely guaranteed nor built entirely into an AI system. We have drawn on the breadth of trust-related literature across multiple disciplines and fields to synthesize knowledge pertaining to interpersonal trust, trust in automation, and risk and trust. Based on this review we have (re)conceptualized trustworthiness in practice as being both (a) perceptual, meaning that a user assesses whether, when, and to what extent AI model output is trustworthy, even if it has been developed in adherence to AI trustworthiness standards, and (b) context-dependent, meaning that a user's perceived trustworthiness and use of an AI model can vary based on the specifics of their situation (e.g., time-pressures for decision-making, high-stakes decisions). We provide our reconceptualization to nuance how trustworthiness is thought about, studied, and evaluated by the AI community in ways that are more aligned with past theoretical research.

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（重新）概念化可信赖的人工智能：变革的基础

开发人员和学者对开发“可信赖的”人工智能（AI）越来越感兴趣。然而，这一目标在实践中很难实现，特别是考虑到信任和可信赖性是复杂的、多方面的概念，不能完全保证也不能完全构建到人工智能系统中。我们借鉴了跨多个学科和领域的信任相关文献的广度，以综合有关人际信任、自动化信任以及风险与信任的知识。在此基础上回顾我们(重新)概念化诚信实际上是(a)的知觉,这意味着用户评估是否时,人工智能模型输出在多大程度上是值得信赖的,即使它在坚持了AI诚信标准,和(b)上下文相关的,也就是说,用户的感知可信度和使用一个AI模型可以根据他们的情况的细节有所不同(例如,时间压力对决策、高风险的决定)。我们提供了我们的重新概念化，以细微差别人工智能社区如何以与过去的理论研究更一致的方式思考、研究和评估可信度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

11.20

自引率

1.40%

发文量

118

审稿时长

8 months

期刊介绍： The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.