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Requirements for trustworthy AI-enabled automated decision-making in the public sector: A systematic review

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  • Agbabiaka, Olusegun
  • Ojo, Adegboyega
  • Connolly, Niall

Abstract

With AI adoption for decision-making in the public sector projected to rise with profound socio-ethical impacts, the need to ensure its trustworthy use continues to attract research attention. We analyze the existing body of evidence and establish trustworthiness requirements for AI-enabled automated decision-making (ADM) in the public sector, identifying eighteen aggregate facets. We link these facets to dimensions of trust in automation and institution-based trust to develop a theory-oriented research framework. We further map them to the OECD AI system lifecycle, creating a practice-focused framework. Our study has theoretical, practical and policy implications. First, we extend the theory on technological trust. We also contribute to trustworthy AI literature, shedding light on relatively well-known requirements like accountability and transparency and revealing novel ones like context sensitivity, feedback and policy learning. Second, we provide a roadmap for public managers and developers to improve ADM governance practices along the AI lifecycle. Third, we offer policymakers a basis for evaluating possible gaps in current AI policies. Overall, our findings present opportunities for further research and offer some guidance on how to navigate the multi-dimensional challenges of designing, developing and implementing ADM for improved trustworthiness and greater public trust.

Suggested Citation

  • Agbabiaka, Olusegun & Ojo, Adegboyega & Connolly, Niall, 2025. "Requirements for trustworthy AI-enabled automated decision-making in the public sector: A systematic review," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:tefoso:v:215:y:2025:i:c:s0040162525001076
    DOI: 10.1016/j.techfore.2025.124076
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