The use of personal data is critical to ensure quality and reliability in scientific research. The new Regulation [European Union (EU)] 2016/679 of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data [general data protection regulation (GDPR)], repealing Directive 95/46/EC, strengthens and harmonises the rules for protecting individuals’ privacy rights and freedoms within and, under certain conditions, outside the EU territory. This new and historic legal milestone both prolongs and updates the EU acquis of the previous Data Protection Directive 95/46/EC. The GDPR fixes both general rules applying to any kind of personal data processing and specific rules applying to the processing of special categories of personal data such as health data taking place in the context of scientific research, this including clinical and translational research areas. This article aims to provide an overview of the new rules to consider where scientific projects include the processing of personal health data, genetic data or biometric data and other kinds of sensitive information whose use is strictly regulated by the GDPR in order to give the main key facts to researchers to adapt their practices and ensure compliance to the EU law to be enforced in May 2018.

We propose a comprehensive analysis of existing concepts of AI coming from different disciplines: Psychology and engineering tackle the notion of intelligence, while ethics and law intend to regulate AI innovations. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems. Relevant concepts are integrated into a matrix intended to help defining more precisely when and how computing tools (programs or devices) may be qualified as AI while highlighting critical features to serve a specific technical, ethical and legal assessment of challenges in AI development. Some adaptations of existing notions of AI characteristics are proposed. The matrix is a risk-based conceptual model designed to allow an empirical, flexible and scalable qualification of AI technologies in the perspective of benefit-risk assessment practices, technological monitoring and regulatory compliance: it offers a structured reflection tool for stakeholders in AI development that are engaged in responsible research and innovation.

Biobanks act as the custodians for the access to and  responsible use of human biological samples and related data that have been generously donated by individuals to serve the public interest and scientific advances in the health research realm. Risk assessment has become a daily practice for biobanks and has been discussed from different perspectives. This paper aims to provide a literature review on risk assessment in order to put together a comprehensive typology of diverse risks biobanks could potentially face. Methodologically set as a typology, the conceptual approach used in this paper is based on the interdisciplinary analysis of scientific literature, the relevant ethical and legal instruments and practices in biobanking to identify how risks are assessed, considered and mitigated. Through an interdisciplinary mapping exercise, we have produced a typology of potential risks in biobanking, taking into consideration the perspectives of different stakeholders, such as institutional actors and publics, including participants and representative organizations. With this approach, we have identified the following risk types: economic, infrastructural, institutional, research community risks and participant’s risks. The paper concludes by highlighting the necessity of an adaptive risk governance as an integral part of good governance in biobanking. In this regard, it contributes to sustainability in biobanking by assisting in the design of relevant risk management practices, where they are not already in place or require an update. The typology is intended to be useful from the early stages of establishing such a complex and multileveled biomedical infrastructure as well as to provide a catalogue of risks for improving the risk management practices already in place.

Governance of health and genomic data access in the context of biobanking is of salient importance in implementing the EU General Data Protection Regulation (GDPR). Various components of data access governance could be considered as ‘organizational measures’ which are stressed in the Article 89(1) GDPR together with technical measures that should be used in order to safeguard rights of the data subjects when processing data under research exemption rules. In this chapter, we address the core elements regarding governance of biobanks in the view of GDPR, including conditions for processing personal data, data access models, oversight bodies and data access agreements. We conclude by highlighting the importance of guidelines and policy documents in helping the biobanks in improving the data access governance. In addition, we stress that it is important to ensure the existing and emerging oversight bodies are equipped with adequate expertise regarding using and sharing health and genomic data and are aware of the associated informational risks.