research-article

The Sanction of Authority: Promoting Public Trust in AI

Authors:

John T. RichardsAuthors Info & Claims

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

Pages 262 - 271

https://doi.org/10.1145/3442188.3445890

Published: 01 March 2021 Publication History

Abstract

Trusted AI literature to date has focused on the trust needs of users who knowingly interact with discrete AIs. Conspicuously absent from the literature is a rigorous treatment of public trust in AI. We argue that public distrust of AI originates from the underdevelopment of a regulatory ecosystem that would guarantee the trustworthiness of the AIs that pervade society. Drawing from structuration theory and literature on institutional trust, we offer a model of public trust in AI that differs starkly from models driving Trusted AI efforts. This model provides a theoretical scaffolding for Trusted AI research which underscores the need to develop nothing less than a comprehensive and visibly functioning regulatory ecosystem. We elaborate the pivotal role of externally auditable AI documentation within this model and the work to be done to ensure it is effective, and outline a number of actions that would promote public trust in AI. We discuss how existing efforts to develop AI documentation within organizations---both to inform potential adopters of AI components and support the deliberations of risk and ethics review boards---is necessary but insufficient assurance of the trustworthiness of AI. We argue that being accountable to the public in ways that earn their trust, through elaborating rules for AI and developing resources for enforcing these rules, is what will ultimately make AI trustworthy enough to be woven into the fabric of our society.

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Cited By

Wischnewski MKrämer NJaniesch CMüller ESchnitzler TNewen C(2024)In Seal We Trust? Investigating the Effect of Certifications on Perceived Trustworthiness of AI SystemsHuman-Machine Communication10.30658/hmc.8.78(141-162)Online publication date: 2024
https://doi.org/10.30658/hmc.8.7
Goh THo S(2024)Trustworthiness of Policymakers, Technology Developers, and Media Organizations Involved in Introducing AI for Autonomous Vehicles: A Public PerspectiveScience Communication10.1177/1075547024124816946:5(584-618)Online publication date: 6-May-2024
https://doi.org/10.1177/10755470241248169
Wan QHu SZhang YWang PWen BLu Z(2024)"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36373618:CSCW1(1-26)Online publication date: 26-Apr-2024
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Index Terms

The Sanction of Authority: Promoting Public Trust in AI
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
2. Social and professional topics
  1. Professional topics
    1. Computing profession

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cover image ACM Conferences

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

March 2021

899 pages

ISBN:9781450383097

DOI:10.1145/3442188

Copyright © 2021 ACM.

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Published: 01 March 2021

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

March 3 - 10, 2021

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Cited By

Wischnewski MKrämer NJaniesch CMüller ESchnitzler TNewen C(2024)In Seal We Trust? Investigating the Effect of Certifications on Perceived Trustworthiness of AI SystemsHuman-Machine Communication10.30658/hmc.8.78(141-162)Online publication date: 2024
https://doi.org/10.30658/hmc.8.7
Goh THo S(2024)Trustworthiness of Policymakers, Technology Developers, and Media Organizations Involved in Introducing AI for Autonomous Vehicles: A Public PerspectiveScience Communication10.1177/1075547024124816946:5(584-618)Online publication date: 6-May-2024
https://doi.org/10.1177/10755470241248169
Wan QHu SZhang YWang PWen BLu Z(2024)"It Felt Like Having a Second Mind": Investigating Human-AI Co-creativity in Prewriting with Large Language ModelsProceedings of the ACM on Human-Computer Interaction10.1145/36373618:CSCW1(1-26)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637361
Bakogeorge AImtiaz SAbu Hantash NManshaei RMazalek ACiolfi LHogan TDöring TJenkins Tvan Dijk JHuron SLi ZCoyle DSigner B(2024)Embodied Machine LearningProceedings of the Eighteenth International Conference on Tangible, Embedded, and Embodied Interaction10.1145/3623509.3633370(1-12)Online publication date: 11-Feb-2024
https://dl.acm.org/doi/10.1145/3623509.3633370
Hoque MMahfuz AKindi MHassan N(2024)Towards Designing a Question-Answering Chatbot for Online News: Understanding Questions and PerspectivesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642007(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642007
Lupetti MMurray-Rust D(2024)(Un)making AI Magic: A Design TaxonomyProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641954(1-21)Online publication date: 11-May-2024
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Schwarz MHinske LMansmann UAlbashiti F(2024)Designing an ML Auditing Criteria Catalog as Starting Point for the Development of a FrameworkIEEE Access10.1109/ACCESS.2024.337576312(39953-39967)Online publication date: 2024
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Laine JMinkkinen MMäntymäki M(2024)Ethics-based AI auditingInformation and Management10.1016/j.im.2024.10396961:5Online publication date: 1-Jul-2024
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Li YXiang B(2024)Reducing organizational inequalities associated with algorithmic controlsDiscover Artificial Intelligence10.1007/s44163-024-00137-04:1Online publication date: 15-May-2024
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Li YGoel S(2024)Making It Possible for the Auditing of AI: A Systematic Review of AI Audits and AI AuditabilityInformation Systems Frontiers10.1007/s10796-024-10508-8Online publication date: 2-Jul-2024
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