This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.

The paper examines today’s debate on the legal status of AI robots, and how often scholars and policy makers confuse the legal agenthood of these artificial agents with the status of legal personhood. By taking into account current trends in the field, the paper suggests a twofold stance. First, policy makers shall seriously mull over the possibility of establishing novel forms of accountability and liability for the activities of AI robots in contracts and business law, e.g., new forms of legal agenthood in cases of complex distributed responsibility. Second, any hypothesis of granting AI robots full legal personhood has to be discarded in the foreseeable future. However, how should we deal with Sophia, which became the first AI application to receive citizenship of any country, namely, Saudi Arabia, in October 2017? Admittedly, granting someone, or something, legal personhood is—as always has been—a highly sensitive political issue that does not simply hinge on rational choices and empirical evidence. Discretion, arbitrariness, and even bizarre decisions play a role in this context. However, the normative reasons why legal systems grant human and artificial entities, such as corporations, their status, help us taking sides in today’s quest for the legal personhood of AI robots. Is citizen Sophia really conscious, or capable of suffering the slings and arrows of outrageous scholars?

All models of legal governance and most regulatory options have to do with ‘top-down’ solutions as an essential ingredient of the approach. Such models may include ‘bottom-up’ forms of self-regulation, such as in forms of ex post regulation, or unenforced self-regulation. This paper focuses on what lies in between such top-down and bottom-up approaches, namely, the middle-out interface of the analysis. Within the EU legal framework, this middle-out layer is mainly associated with forms of co-regulation, as defined by Recital 44 of the 2010 AVMS Directive and Article 5(2) of the GDPR. However, there are also additional models on how we should grasp the middle-out layer of legal regulation, as shown by the debates on the governance of AI and the Web of Data. For example, the debates on issues such as monitored self-regulation, coordination mechanisms for good AI governance, and ‘wind-rose’ models for the Web of Data make it clear that co-regulation is not the only alternative to both bottom-up and top-down approaches. From a methodological viewpoint, the middle-out approach sheds light on three different kinds of issues that regard (i) how to strike a balance between multiple regulatory systems; (ii) how to align primary and secondary rules of the law; and (iii) how to properly coordinate bottom-up and top-down policy choices. The increasing complexity of technological regulation recommends new models of governance that revolve around this middle-out analytical ground.

Purpose: This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon. Methods: The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health. Results: Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem. Conclusions: The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening. Graphical