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Cited by 261Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)Safety Systems Engineering in Autonomy

Trustworthy ai: A computational perspective

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Liu, Haochen, Wang, Yiqi, Fan, Wenqi, Liu, Xiaorui, Li, Yaxin, Jain, Shaili, Liu, Yunhao, Jain, Anil +1 more·2023·ACM Transactions on Intelligent Systems and Technology
DOI 10.1145/3546872
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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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  • Hardware-in-the-loop simulations can help developers understand system performance when run on chips, sensors, and actuators in a simulated environment, and are particularly helpful for latency- and power-critical systems.

    “Hardware-in-the-loop simulations can help developers understand system performance when it is run on chips, sensors, and actuators in a simulated environment, and is particularly helpful for latency- and power-critical systems like autonomous driving systems [49, 53].”

    From Trustworthy AI: From Principles to Practices (2023) · cites this paper as [49]

    Section 8.2 describes estimating CNN power and runtime before deployment, and section 8.1.3 discusses AI hardware, but neither describes hardware-in-the-loop simulation. Searches for chips, sensors, actuators, simulated environments, and latency- or power-critical autonomous driving found no passage addressing the attributed claim. (LLM, conf=0.72)

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Authors
Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J.
Affiliations
  • Michigan State University (US)
  • Hong Kong Polytechnic University (HK)
  • Twitter (United States) (US)
  • Tsinghua University (CN)
ORCID
Venue
ACM Transactions on Intelligent Systems and Technology
Publisher
Association for Computing Machinery (ACM)
Published
2022-11-09
Volume
14
Issue
1
Pages
1-59
ISSN
  • 2157-6904 (ISSN-L)
  • 2157-6912
DOI
10.1145/3546872
OpenAlex
W3182221256
Cited by
261
References
403
Citations by year
2026: 42 · 2025: 85 · 2024: 76 · 2023: 45 · 2022: 12 · 2021: 1
FWCI
63.10
Type
journal article
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
  • ACM Transactions on Intelligent Systems and Technology (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (IIS1714741, CNS1815636, IIS1845081, IIS1907704, DRL2025244, IIS1928278, IIS1955285, IOS2107215, IOS2035472)
  • Army Research Office (W911NF-21-1-0198)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
Keywords
trustworthy AI, AI safety, robustness, algorithmic fairness, non-discrimination, explainability, privacy, accountability, auditability, environmental impact of AI, safety-critical systems

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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