Ad Machina: Partisanship and Support for Delegating Government Decisions to Autonomous Algorithms
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DOI: 10.31219/osf.io/rnj5h_v2
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- Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2023. "The extent of algorithm aversion in decision-making situations with varying gravity," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-21, February.
- Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
- Waggoner Philip D. & Kennedy Ryan & Le Hayden & Shiran Myriam, 2019. "Big Data and Trust in Public Policy Automation," Statistics, Politics and Policy, De Gruyter, vol. 10(2), pages 115-136, December.
- James N. Druckman & Samara Klar & Yanna Krupnikov & Matthew Levendusky & John Barry Ryan, 2021. "Affective polarization, local contexts and public opinion in America," Nature Human Behaviour, Nature, vol. 5(1), pages 28-38, January.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-AIN-2025-07-21 (Artificial Intelligence)
- NEP-EXP-2025-07-21 (Experimental Economics)
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