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

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. • We determined 18 aggregate trustworthiness requirements for AI-enabled ADM. • These requirements were mapped to six trust dimensions to create a theory-oriented research framework. • For practice, we mapped the requirements to the OECD AI lifecycle. • Context sensitivity was found to be key to the trustworthiness of AI-enabled ADM. • AI algorithms require multiple forms of accountability to be trustworthy