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

Olusegun Agbabiaka, Adegboyega Ojo and Niall Connolly

Technological Forecasting and Social Change, 2025, vol. 215, issue C

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.

Keywords: Trustworthy AI; Trustworthiness; Trust; Systematic review; Automated decision-making; ADM (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162525001076
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:215:y:2025:i:c:s0040162525001076

DOI: 10.1016/j.techfore.2025.124076

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().