How to build an ecosystem of trust in digital health? The availability of large amounts of personal data, combined with AI and ML capacities, Internet of Things and strong computational platforms, has the potential to transform healthcare systems in a disruptive way. The emergence of personalised medicine offers opportunities and raises new legal, ethical and societal challenges. A silent shift towards data-driven preventive and personalised medicine may improve diagnosis and therapies while reducing public health costs. In order to build trust, risks such as data breaches, privacy issues, discrimination and eugenics must be addressed. This chapter presents the disruptive nature of AI and ML technologies in healthcare, and makes specific recommendations to build a trustworthy digital health system. Special attention is given to governance by international institutions as well as key principles like transparency, accountability and decision-making processes in a medical context. We first identify the key parameters to advance the field of digital health in a responsible way. Second, we propose possible solutions to shape a sound policy in digital health taking into account a rights-based governance framework. The last part of the chapter is dedicated to the accountability scheme.

Law enforcement agencies are increasingly using algorithmic predictive policing systems to forecast criminal activity and allocate police resources. For instance, New York, Chicago, and Los Angeles use predictive policing systems built by private actors, such as PredPol, Palantir and Hunchlab. However, predictive policing is not a panacea to eradicate crime and many concerns raised on the inefficiency, risk of discrimination, as well as lack of transparency. The necessity of protecting fundamental rights has to be reiterated in the algorithmic society. To do it, adapted tools must be deployed to ensure proper enforcement of fundamental rights. Some ethical principles need to be put in place in order to effectively protect fundamental rights and to reinforce them. I argue that while the European constitutional and ethical framework is theoretically sufficient, other tools must be adopted to guarantee the enforcement of Fundamental Rights and Ethical Principles in practice to provide a robust framework, for keeping human rights a central place. Algorithmic Impact Assessment (AIA) constitutes an interesting way to provide a concrete governance of automated decision-making.

Artificial intelligence (AI) is one of many digital technologies currently under development.1 In recent years, it is having increasing repercussions in the field of law. These repercussions go beyond the traditional effect of an economic and industrial evolution. Indeed, the epochal industrial transformations and paradigmatic shifts it generates in many sectors have, from a legal perspective, a structural impact on legal rules and on legal practice. Moreover, the speed of these transformations also impacts on the regulatory response that a legislator is able to provide. In point of fact, rather than running the risk of new legislation rapidly becoming obsolete, regulators around the world have preferred so far to take their time to observe the changes unfolding in current technologies, and to assess their impacts from the legal point of view, before proposing any specific courses of action. Although legal experts, contrary to ethicists, have traditionally shown little interest in AI, algorithms, machine learning and so forth, it is now virtually impossible for them to ignore the impact of AI on the law, and more specifically, the question of whether actual legal rules and regulations can cope with the changes taking place in the economy and in the society, on one hand, and whether the use of AI tools in legal practice is compatible with the founding principles of our legal orders, on the other hand. If new rules are needed, lawyers will have to define their content and how to make sure they are suitable for the long term, in a context of rapidly changing technologies.

The development of a policy framework for the sustainable and ethical use of artificial intelligence (AI) techniques has gradually become one of the top policy priorities in developed countries as well as in the international context, including the G7 and G20 and work within the Organisation for Economic Cooperation and Development (OECD), the World Economic Forum, and the International Telecommunications Union. Interestingly, this mounting debate has taken place with very little attention to the definition of what AI is and its phenomenology in the real world, as well as its expected evolution. Politicians evoke the imminent takeover of smart autonomous robots; entrepreneurs announce the end of mankind, or the achievement of immortality through brain upload; and academics fight over the prospects of Artificial General Intelligence, which appears inevitable to some, and preposterous to others. In all this turmoil, governments developed the belief that, as both Vladimir Putin and Xi Jinping recently put it, the country that will lead in AI will, as a consequence, come to dominate the world. As AI gains positions in the ranking of top government priorities, a digital arms race has also emerged, in particular between the United States and China. This race bears far-reaching consequences when it comes to earmarking funds for research, innovation, and investment on AI technologies: gradually, AI becomes an end, rather than a means, and military and domestic security applications are given priority over civilian use cases, which may contribute more extensively to social and environmental sustainability. As of today, one could argue that the top priority in US AI policy is contrasting the rise of China, and vice versa.1