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This paper investigates the deployment of Artificial Intelligence (AI) in the Swedish Public Employment Service (PES), focusing on the concept of trustworthy AI in public decision-making. Despite Sweden's advanced digitalization efforts and the widespread application of AI in the public sector, our study reveals significant gaps between theoretical ambitions and practical outcomes, particularly in the context of AI's trustworthiness. We employ a robust theoretical framework comprising Institutional Theory, the Resource-Based View (RBV), and Ambidexterity Theory, to analyze the challenges and discrepancies in AI implementation within PES. Our analysis shows that while AI promises enhanced decision-making efficiency, the reality is marred by issues of transparency, interpretability, and stakeholder engagement. The opacity of the neural network used by the agency to assess jobseekers’ need for support and the lack of comprehensive technical understanding among PES management contribute to the challenges in achieving transparent and interpretable AI systems. Economic pressures for efficiency often overshadow the need for ethical considerations and stakeholder involvement, leading to decisions that may not be in the best interest of jobseekers. We propose recommendations for enhancing AI's trustworthiness in public services, emphasizing the importance of stakeholder engagement, particularly involving jobseekers in the decision-making process. Our study advocates for a more nuanced balance between the use of advanced AI technologies and the leveraging of internal resources such as skilled personnel and organizational knowledge. We also highlight the need for improved AI literacy among both management and personnel to effectively navigate AI's integration into public decision-making processes. Our findings contribute to the ongoing debate on trustworthy AI, offering a detailed case study that bridges the gap between theoretical exploration and practical application. By scrutinizing the AI implementation in the Swedish PES, we provide valuable insights and guidelines for other public sector organizations grappling with the integration of AI into their decision-making processes.
Berman et al. (Fri,) studied this question.