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February 14, 2026Public Administration1 citationsOpen Access

Holding AI Accountable Like Herding Cats: The Contingent Impact on the Legitimacy of Algorithmic Bureaucracy

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RLRui LiuYHYanbing Han

Key Points

  • This study aims to clarify the relationship between AI accountability, effectiveness, and legitimacy in public decision-making.
  • Conducted three survey experiments with 1135 participants in China
  • Examined perceptions of AI accountability and effectiveness in different decision contexts
  • Analyzed interactions between AI accountability and decision outcomes
  • Accountability is crucial when AI decisions result in losses for citizens
  • Effectiveness has a more significant role when outcomes are favorable to citizens
  • Interaction effects between accountability and effectiveness vary depending on decision outcomes

Abstract

ABSTRACT The rise of artificial intelligence in public decision‐making is reshaping state legitimacy by shifting administrative discretion from human bureaucracies to algorithmic systems. While research has explored AI accountability and legitimacy deficits, how they are related across different decision contexts remains unclear. Drawing on bureaucratic legitimacy, procedural fairness, and forum drifting theories, this study examines how AI accountability and effectiveness shape legitimacy perceptions, depending on decision outcomes. Using three survey experiments with 1135 participants in China, we find that accountability is most crucial when AI decisions introduce losses to citizens, whereas effectiveness plays a greater role when outcomes are positive to them. Additionally, the interaction effects between AI accountability and effectiveness are also contingent on decision outcomes. These findings advance AI governance research by highlighting the conditions under which algorithmic legitimacy is strengthened or weakened, emphasizing the need for tailored accountability and effectiveness strategies based on decision outcomes.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/699011712ccff479cfe58106https://doi.org/10.1111/padm.70043
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