Corbett-Davies, S., Pierson, E., Feller, A., Goel, S. and Huq, A. Algorithmic decision making and the cost of fairness. In Proceedings of the 23^rd ACM SIGKDD Intern. Conf. Knowledge Discovery and Data Mining. ACM Press, 2017, 797--806.

Digital Library

Google Scholar

[5]

Critcher, C.R. and Gilovich, T. Incidental environmental anchors. J. Behavioral Decision Making 21, 3 (2008), 241--251.

Crossref

Google Scholar

[6]

Dressel, J. and Farid, H. The accuracy, fairness, and limits of predicting recidivism. Science Advances 4, 1 (2018), eaao5580.

Crossref

Google Scholar

[7]

Englich, B., Mussweiler, T. and Strack, F. Playing dice with criminal sentences: the influence of irrelevant anchors on experts' judicial decision making. Personality and Social Psychology Bulletin 32, 2 (2006), 188--200.

Crossref

Google Scholar

[8]

Furnham, A. and Boo, H.C. A literature review of the anchoring effect. The J. Socio-Economics 40, 1 (2011), 35--42.

Crossref

Google Scholar

[9]

Goldstein, I.M., Lawrence, J. and Miner, A.S. Human-machine collaboration in cancer and beyond: The Centaur Care Model. JAMA Oncology 3, 10 (2017), 1303.

Google Scholar

[10]

Green, K.C. and Armstrong, J.S. Evidence on the effects of mandatory disclaimers in advertising. J. Public Policy & Marketing 31, 2 (2012), 293--304.

Crossref

Google Scholar

[11]

Horvitz, E. and Paek, T. Complementary computing: policies for transferring callers from dialog systems to human receptionists. User Modeling and User-Adapted Interaction 17, 1--2 (2007), 159--182.

Crossref

Google Scholar

[12]

Johnson, R.C. Overcoming AI bias with AI fairness. Commun. ACM (Dec. 6, 2018).

Google Scholar

[13]

Jukier, R. Inside the judicial mind: exploring judicial methodology in the mixed legal system of Quebec. European J. Comparative Law and Governance (Feb. 2014).

Google Scholar

[14]

Kahneman, D. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.

Google Scholar

[15]

Mussweiler, T. and Strack, F. Numeric judgments under uncertainty: the role of knowledge in anchoring. J. Experimental Social Psychology 36, 5 (2000), 495--518.

Crossref

Google Scholar

[16]

Northcraft, G.B. and Neale, M.A. Experts, amateurs, and real estate: an anchoring-and-adjustment perspective on property pricing decisions. Organizational Behavior and Human Decision Processes 39, 1 (1987), 84--97.

Crossref

Google Scholar

[17]

Shaw, A.D., Horton, J.J. and Chen, D.L. Designing incentives for inexpert human raters. In Proceedings of the ACM Conf. Computer-supported Cooperative Work. ACM Press, 2011, 275--284.

Digital Library

Google Scholar

[18]

State v Loomis, 2016.

Google Scholar

[19]

Tversky, A. and Kahneman, D. Judgment under uncertainty: Heuristics and biases. Science 185, 4157 (1974), 1124--1131.

Crossref

Google Scholar

[20]

Wansink, B., Kent, R.J. and Hoch, S.J. An anchoring and adjustment model of purchase quantity decisions. J. Marketing Research 35, 1 (1998), 71.

Crossref

Google Scholar

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Index Terms

The effects of mixing machine learning and human judgment
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Human-centered computing

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Communications of the ACM Volume 62, Issue 11

November 2019

136 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/3368886

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Andrew A. Chien
Association for Computing Machinery, New York, NY

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Published: 24 October 2019

Published in CACM Volume 62, Issue 11

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Guo ZWu YHartline JHullman J(2024)A Decision Theoretic Framework for Measuring AI RelianceProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658901(221-236)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658901
Litvinova YMikalef PLuo X(2024)Framework for human–XAI symbiosis: extended self from the dual-process theory perspectiveJournal of Business Analytics10.1080/2573234X.2024.23963667:4(224-255)Online publication date: 25-Sep-2024
https://doi.org/10.1080/2573234X.2024.2396366
Vaccaro MAlmaatouq AMalone T(2024)When combinations of humans and AI are useful: A systematic review and meta-analysisNature Human Behaviour10.1038/s41562-024-02024-18:12(2293-2303)Online publication date: 28-Oct-2024
https://doi.org/10.1038/s41562-024-02024-1
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The Effects of Mixing Machine Learning and Human Judgment: Collaboration between humans and machines does not necessarily lead to better outcomes.

Ball bonding inspections using a conjoint framework with machine learning and human judgement

Introduction to the Special Issue on Human-Centered Machine Learning

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