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Ad Machina: Partisanship and Support for Delegating Government Decisions to Autonomous Algorithms

Author

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  • DiGiuseppe, Matthew

    (Leiden University)

  • Paula, Katrin
  • Rommel, Tobias

Abstract

Under which conditions are citizens willing to delegate government responsibilities to artificial intelligence? We hypothesize that the identity of incumbent policymakers impacts public support for delegating decisions to AI. In highly polarized societies, AI has the potential to be perceived as a decision maker with apolitical or less partisan motivations in governance decisions. We thus reason that individuals will prefer co-partisans to AI or algorithmic decision making. However, a switch to AI decision making will have more public support when out-partisans hold policy control. To test our hypothesis, we fielded a survey experiment in the summer of 2024 that asked about 2500 respondents in the US to register their support for AI making the most important economic decision in the world -- the setting of the base interest rate by the US Federal Reserve. The basis of our experimental treatments is the fact that Jerome Powell, the current chair of the Fed, was appointed first by President Trump, a Republican, and later re-appointed by President Biden, a Democrat. We find that when we inform respondents that Powell was appointed by a president from another party, support for delegation to AI increases compared to the condition when the Fed chair is appointed by a co-partisan. The complier average causal effect (CACE) indicates that change perception of the Fed Chair to an outpartisan increases support for delegating to AI by over 45%.

Suggested Citation

  • DiGiuseppe, Matthew & Paula, Katrin & Rommel, Tobias, 2025. "Ad Machina: Partisanship and Support for Delegating Government Decisions to Autonomous Algorithms," SocArXiv rnj5h_v2, Center for Open Science.
    • Handle: RePEc:osf:socarx:rnj5h_v2
    DOI: 10.31219/osf.io/rnj5h_v2
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
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