Risk assessment is an evidence-based analytical framework used to evaluate research findings related to environmental and public health decision-making. Different routines have been adopted for assessing the potential risks posed by substances and products to human health. In general, the traditional paradigm is a hazard-driven approach, based on a monocausal toxicological perspective. Questions have been raised about the applicability of the general chemical risk assessment approach in the specific case of nanomaterials. Most scientists and stakeholders assume that the current standard (...) methods are in principle suitable, but point out that experimental aspects and practical guidelines need specific adaptations. Beyond this laboratory level, risk assessment of nanomaterials also faces a number of substantive and procedural limitations, which are intrinsically attributed to the general orthodoxy of the risk assessment concept. Moreover, the developed formalism used to organize scientific knowledge is closely interlinked with the underlying governance design and the mode of interaction between the two spheres of ‘science’ and ‘decisions’. This contribution will provide a closer look at the evolution of different institutional settings for risk assessment in the context of decision-making. Improved risk governance frameworks with different narratives, process designs and procedural elements will be compared. The question of a general principle of enhanced organization of risk assessment will be discussed taking account of the barriers of substantive and procedural limitations in the special case of nanomaterials. (shrink)

Approaches aimed at regulating artificial intelligence (AI) include a particular form of risk regulation, i.e. a risk-based approach. The most prominent example is the European Union’s Artificial Intelligence Act (AI Act). This article addresses the challenges for adequate risk regulation that arise primarily from the specific type of risks involved, i.e. risks to the protection of fundamental rights and fundamental societal values. This is mainly due to the normative ambiguity of such rights and societal values when attempts are made to (...) select, interpret, specify or operationalise them for the purposes of risk assessments and risk mitigation. This is exemplified by (1) human dignity, (2) informational self-determination, data protection and privacy, (3) anti-discrimination, fairness and justice, and (4) the common good. Normative ambiguities require normative choices, which are assigned to different actors under the regime of the AI Act. Particularly critical normative choices include selecting normative concepts by which to operationalise and specify risks, aggregating and quantifying risks (including the use of metrics), balancing value conflicts, setting levels of acceptable risks, and standardisation. To ensure that these normative choices do not lack democratic legitimacy and to avoid legal uncertainty, further political processes and scientific debates are suggested. (shrink)