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Humble AI

Authors:

Bran Knowles,

Jason D'Cruz,

John T. Richards,

Kush R. VarshneyAuthors Info & Claims

Communications of the ACM, Volume 66, Issue 9

Pages 73 - 79

https://doi.org/10.1145/3587035

Published: 23 August 2023 Publication History

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Abstract

An effort to bring artificial intelligence into better alignment with our moral aims and finally realize the vision of superior decision making through AI.

References

[1]

Alfano, M. Friendship and the Structure of Trust (2016).

Crossref

Google Scholar

[2]

Bakker, M.A. et al. Beyond reasonable doubt: Improving fairness in budget-constrained decision making using confidence thresholds. In Proceedings of the AAAI/ACM Conf. on AI, Ethics, and Society (July 2021), 346--356.

Google Scholar

[3]

Barassi, V. et al. David Graeber, bureaucratic violence, and the critique of surveillance capitalism. Annals of the Fondazione Luigi Einaudi 55 (2021), 237--254.

Google Scholar

[4]

Bartlett, P.L. and Wegkamp, M.H. Classification with a reject option using a hinge loss. J. of Machine Learning Research 9 (August 2008), 1823--1840.

Digital Library

Google Scholar

[5]

Bhatt, U. et al. Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty. In Proceedings of the AAAI/ACM Conf. on AI, Ethics, and Society (July 2021), 401--413.

Google Scholar

[6]

Bu, Y. et al. Fair selective classification via sufficiency. In Proceedings of the Intern. Conf. on Machine Learning (July 2021), 6076--6086.

Google Scholar

[7]

Cover, T.M. and Joy, A.A. Elements of Information Theory, Wiley (2012).

Digital Library

Google Scholar

[8]

D'Cruz, J. Humble trust. Philosophical Studies 176, 4 (2019), 933--953.

Google Scholar

[9]

D'Cruz, J.R. et al. The empathy gap: Why AI can forecast behavior but cannot assess trustworthiness. AAAI 2022 Fall Symp. Series.

Google Scholar

[10]

Fetchenhauer, D. and Dunning, D. Why so cynical? Asymmetric feedback underlies misguided skepticism regarding the trustworthiness of others. Psychological Science 21, 2 (2010), 189--193.

Crossref

Google Scholar

[11]

Guo, C. et al. On calibration of modern neural networks. In Proceedings of the Intern. Conf. on Machine Learning (August 2017), 1321--1330.

Google Scholar

[12]

How security clearance credit check rules impact many military service members. Consolidated Credit (2018); https://tinyurl.com/2mr2uf9y.

Google Scholar

[13]

Jones, E.E. and Harris, V.A. The attribution of attitudes. J. of Experimental Social Psychology 3, 1 (1967), 1--24.

Crossref

Google Scholar

[14]

Jones, K. Trust, distrust, and affective looping. Philosophical Studies 176, 4 (2019), 955--968.

Crossref

Google Scholar

[15]

Knowles, B. et al. The many facets of trust in AI: Formalizing the relation between trust and fairness, accountability, and transparency. (2022); arXiv preprint arXiv: 2208.00681.

Google Scholar

[16]

Kurt, D. The side effects of bad credit. Investopedia (2021); https://bit.ly/43xYuJp.

Google Scholar

[17]

Marks, P. Algorithmic hiring needs a human face. Communications of the ACM 65, 3 (2022), 17--19.

Digital Library

Google Scholar

[18]

McGeer, V. Developing trust. Philosophical Explorations 5, 1 (2002), 21--38.

Crossref

Google Scholar

[19]

McGeer, V. Trust, hope and empowerment. Australasian J. of Philosophy 86, 2 (2008), 237--254.

Crossref

Google Scholar

[20]

Moldovan, T.M. and Abbeel, P.P. Safe exploration in Markov decision processes. In Proceedings of the Intern. Conf. on Machine Learning (June 2012), 1451--1458.

Google Scholar

[21]

O'Neill, O. A question of trust. Lecture 2: Trust and Terror. The Reith Lectures, BBC (2002).

Google Scholar

[22]

Pettit, P. The cunning of trust. Philosophy Public Affairs 24, 3 (1995), 202--225.

Crossref

Google Scholar

[23]

Pope, D.G. and Sydnor, J.R. Implementing antidiscrimination policies in statistical profiling models. American Economic J. Economic Policy 3, 3 (2011), 206--231.

Crossref

Google Scholar

[24]

Public attitudes to data and AI: Tracker survey. Centre for Data Ethics and Innovation. (2022); https://tinyurl.com/ycfjxb8d.

Google Scholar

[25]

Slepian, M.L. and Ames, D.R. Internalized impressions: The link between apparent facial trustworthiness and deceptive behavior is mediated by targets' expectations of how they will be judged. Psychological Science 27, 2 (2016), 282--288.

Crossref

Google Scholar

[26]

Speakman, S. et al. Three population covariate shift for mobile phone-based credit-scoring. In Proceedings of the ACM Conf. on Computing and Sustainable Societies. (June 2018), 20.

Google Scholar

[27]

The public don't trust computer algorithms to make decisions about them, survey finds. BCS, The Chartered Institute for IT (2020); https://tinyurl.com/2ryf9rvs.

Google Scholar

[28]

Varshney, L.R. and Varshney, K.R. Decision making with quantized priors leads to discrimination. In Proceedings of the IEEE 105, 2 (2016), 241--255.

Google Scholar

[29]

Wei, D. Decision-making under selective labels: Optimal finite-domain policies and beyond. In Proceedings of the Inter. Conf. on Machine Learning (July 2021), 11035--11046.

Google Scholar

[30]

Wierzbicki, A.P. A mathematical basis for satisficing decision making. Mathematical Modelling 3, 5 (1982), 391--405.

Crossref

Google Scholar

Cited By

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Fledderjohann JKnowles BMiller E(2024)Algorithmic Reproductive JusticeProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658903(254-266)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658903
Vasconcelos MCavique L(2024)Mitigating false negatives in imbalanced datasets: An ensemble approachExpert Systems with Applications10.1016/j.eswa.2024.125674(125674)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125674
Ficarra F(2024)Machine Learning and Human UnlearningExtended Selected Papers of the 14th International Conference on Information, Intelligence, Systems, and Applications10.1007/978-3-031-67426-6_4(72-118)Online publication date: 14-Aug-2024
https://doi.org/10.1007/978-3-031-67426-6_4

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Humble AI

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Published In

Communications of the ACM Volume 66, Issue 9

September 2023

97 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3617556

Editor:
James Larus
Association for Computing Machinery, New York, NY

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 23 August 2023

Published in CACM Volume 66, Issue 9

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Fledderjohann JKnowles BMiller E(2024)Algorithmic Reproductive JusticeProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658903(254-266)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658903
Vasconcelos MCavique L(2024)Mitigating false negatives in imbalanced datasets: An ensemble approachExpert Systems with Applications10.1016/j.eswa.2024.125674(125674)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.125674
Ficarra F(2024)Machine Learning and Human UnlearningExtended Selected Papers of the 14th International Conference on Information, Intelligence, Systems, and Applications10.1007/978-3-031-67426-6_4(72-118)Online publication date: 14-Aug-2024
https://doi.org/10.1007/978-3-031-67426-6_4

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Recommendations

Contemporary cybernetics and its facets of cognitive informatics and computational intelligence

Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systems

Guest Editors' Introduction: The New Frontier of Human-Level Artificial Intelligence

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