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Indecision Modeling

Publication ,  Journal Article
McElfresh, DC; Chan, L; Doyle, K; Sinnott-Armstrong, W; Conitzer, V; Borg, JS; Dickerson, JP
Published in: 35th AAAI Conference on Artificial Intelligence, AAAI 2021
January 1, 2021

AI systems are often used to make or contribute to important decisions in a growing range of applications, including criminal justice, hiring, and medicine. Since these decisions impact human lives, it is important that the AI systems act in ways which align with human values. Techniques for preference modeling and social choice help researchers learn and aggregate peoples’ preferences, which are used to guide AI behavior; thus, it is imperative that these learned preferences are accurate. These techniques often assume that people are willing to express strict preferences over alternatives; which is not true in practice. People are often indecisive, and especially so when their decision has moral implications. The philosophy and psychology literature shows that indecision is a measurable and nuanced behavior—and that there are several different reasons people are indecisive. This complicates the task of both learning and aggregating preferences, since most of the relevant literature makes restrictive assumptions on the meaning of indecision. We begin to close this gap by formalizing several mathematical indecision models based on theories from philosophy, psychology, and economics; these models can be used to describe (indecisive) agent decisions, both when they are allowed to express indecision and when they are not. We test these models using data collected from an online survey where participants choose how to (hypothetically) allocate organs to patients waiting for a transplant.

Duke Scholars

Published In

35th AAAI Conference on Artificial Intelligence, AAAI 2021

Publication Date

January 1, 2021

Volume

7

Start / End Page

5975 / 5983
 

Citation

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McElfresh, D. C., Chan, L., Doyle, K., Sinnott-Armstrong, W., Conitzer, V., Borg, J. S., & Dickerson, J. P. (2021). Indecision Modeling. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 7, 5975–5983.
McElfresh, D. C., L. Chan, K. Doyle, W. Sinnott-Armstrong, V. Conitzer, J. S. Borg, and J. P. Dickerson. “Indecision Modeling.” 35th AAAI Conference on Artificial Intelligence, AAAI 2021 7 (January 1, 2021): 5975–83.
McElfresh DC, Chan L, Doyle K, Sinnott-Armstrong W, Conitzer V, Borg JS, et al. Indecision Modeling. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021 Jan 1;7:5975–83.
McElfresh, D. C., et al. “Indecision Modeling.” 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 7, Jan. 2021, pp. 5975–83.
McElfresh DC, Chan L, Doyle K, Sinnott-Armstrong W, Conitzer V, Borg JS, Dickerson JP. Indecision Modeling. 35th AAAI Conference on Artificial Intelligence, AAAI 2021. 2021 Jan 1;7:5975–5983.

Published In

35th AAAI Conference on Artificial Intelligence, AAAI 2021

Publication Date

January 1, 2021

Volume

7

Start / End Page

5975 / 5983