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October 7, 2025Public Administration5 citationsOpen Access

AI in Public Decision‐Making: A Philosophical and Practical Framework for Assessing and Weighing Harm and Benefit

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KLKarl de Fine LichtAFAnna Folland

Key Points

  • The proposed framework enhances public decision-making by integrating moral philosophy and AI principles, improving transparency.
  • It operationalizes harm and benefit through well-being measures and addresses value conflicts in a structured way.
  • The framework's practical applicability is illustrated by a case example related to the Dutch childcare benefits scandal, providing real-world relevance.
  • Implementation challenges like cognitive biases and political trade-offs are discussed, pointing to necessary empirical validation approaches.

Abstract

ABSTRACT Artificial intelligence (AI) is increasingly used in public decision‐making; yet existing governance tools often lack clear definitions of harm and benefit, practical methods for weighing competing values, and guidance for resolving value conflicts. This paper presents a five‐step framework that integrates moral philosophy, trustworthy AI principles, and procedural justice into a coherent decision process for public administrators. The framework operationalizes harm and benefit through multidimensional well‐being measures, applies normative principles such as harm–benefit asymmetry, incorporates technical assessment criteria, and offers structured methods for resolving both derivative and fundamental value conflicts. A worked example, based on the Dutch childcare benefits scandal, illustrates its application under real‐world constraints. Comparative analysis positions the framework alongside established tools, highlighting its added value in combining normative reasoning with procedural legitimacy. The paper also discusses implementation challenges, including cognitive biases, institutional inertia, and political trade‐offs, and suggests empirical approaches for validation. By linking philosophical depth with practical usability, the framework supports transparent, context‐sensitive governance of AI in the public sector.

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Cite This Study

Licht et al. (2025) studied this question.

synapsesocial.com/papers/68e5a0557f330f793683efe9https://doi.org/10.1111/padm.70029
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