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Trustworthy AI: A Computational Perspective

Published:09 November 2022Publication History
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Abstract

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone’s daily life and profoundly altering the course of human society. The intention behind developing AI was and is to benefit humans by reducing labor, increasing everyday conveniences, and promoting social good. However, recent research and AI applications indicate that AI can cause unintentional harm to humans by, for example, making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against a group or groups. Consequently, trustworthy AI has recently garnered increased attention regarding the need to avoid the adverse effects that AI could bring to people, so people can fully trust and live in harmony with AI technologies.

A tremendous amount of research on trustworthy AI has been conducted and witnessed in recent years. In this survey, we present a comprehensive appraisal of trustworthy AI from a computational perspective to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex subject, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Nondiscrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.

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  1. Trustworthy AI: A Computational Perspective

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          cover image ACM Transactions on Intelligent Systems and Technology
          ACM Transactions on Intelligent Systems and Technology  Volume 14, Issue 1
          February 2023
          487 pages
          ISSN:2157-6904
          EISSN:2157-6912
          DOI:10.1145/3570136
          • Editor:
          • Huan Liu
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          Publication History

          • Published: 9 November 2022
          • Online AM: 12 July 2022
          • Accepted: 7 June 2022
          • Revised: 30 May 2022
          • Received: 1 September 2021
          Published in tist Volume 14, Issue 1

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