Governments are increasingly using sophisticated self-learning algorithms to automate and standardize decision-making on a large scale. However, despite aspirations for predictive data and more efficient decision-making, the introduction of artificial intelligence (AI) also gives rise to risks and creates a potential for harm. The attribution of responsibility to individuals for the harm caused by these novel socio-Technical decision-making systems is epistemically and normatively challenging. The conditions necessary for individuals to be adequately held responsible-moral agency, freedom, control, and knowledge, can be undermined by the introduction of algorithmic decision-making. Thereby responsibility gaps are created where seemingly no one is sufficiently responsible for the system's outcome. We turn this challenge to adequately attribute responsibility into a design challenge to design for these responsibility conditions. Drawing on philosophical responsibility literature, we develop a conceptual framework to scrutinize the task responsibilities of actors involved in the (re-)design and application of algorithmic decision-making systems. This framework is applied to an empirical case study involving AI in automated governmental decision-making. We find that the framework enables the critical assessment of a socio-Technical system's design for responsibility and provides valuable insights to prevent future harm. The article addresses the current academic and empirical lack of philosophical insights to understand and design for responsibilities in novel algorithmic ICT systems. @en

Collaborative and network governance assume that network management and trust matter for network outcomes. We test this assumption by conducting a meta-analysis of public administration studies investigating the correlation between network management and network outcomes (50 effect sizes), and trust and network outcomes (28 effect sizes). While both matter for achieving network outcomes across countries, trust matters most. Trust is particularly important for achieving process outcomes and multiple network management strategies combined are more effective than separate single strategies. A research agenda centred on complex modelling, comparative research and using mixed, multisource, experimental and longitudinal data is stipulated in conclusion.@en

Collaborative and network governance assume that network management and trust matter for network outcomes. We test this assumption by conducting a meta-analysis of public administration studies investigating the correlation between network management and network outcomes (50 effect sizes), and trust and network outcomes (28 effect sizes). While both matter for achieving network outcomes across countries, trust matters most. Trust is particularly important for achieving process outcomes and multiple network management strategies combined are more effective than separate single strategies. A research agenda centred on complex modelling, comparative research and using mixed, multisource, experimental and longitudinal data is stipulated in conclusion.@en

Artificial intelligence (AI) adoption by public sector organizations (PSOs) introduces various ethical risks stemming from a lack of integrating human values into AI design. Addressing these ethical risks is a complex collective responsibility among designers, developers, risk experts, and public sector managers. Embedding these risks in existing risk management practices is crucial for responsible AI adoption, as emphasized by the legal requirements of the EU AI Act. However, the responsibility for managing these ethical risks is often unclear. Public sector organizations face unique challenges due to the complex, uncertain, and rapidly evolving nature of AI technologies, further complicating the management of ethical risks. This paper explores using the Three Lines of Defense (TLoD) risk management model to understand and address these ethical risks in public sector AI adoption. The TLoD model structures risk management across three lines: operational management, risk oversight and compliance, and internal audit. This framework helps to distribute and integrate the collective responsibility for ethical AI risk management within public sector organizations, emphasizing alignment and collaboration among different actors. Through an exploratory study involving a survey and semi-structured interviews with professionals responsible for AI-related risk management in Dutch public sector organizations, we assess the TLoD model’s usefulness in addressing ethical AI risks. The study examines the challenges and opportunities in applying the TLoD model to manage ethical risks and identifies the potential gaps in responsibility and oversight. The findings suggest that while the TLoD model offers a valuable lens for distributing risk management responsibilities, there are limitations in addressing the emergent and complex nature of ethical risks in AI adoption.@en