Risk assessment is a scientific exercise that aims at anticipating hazards. By convention, it entails a specification of this hazard, the collection of data about known occurrences, and various calculations allowing to extrapolate the frequency, severity, probability of future occurrences for particular persons or organizations from a baseline of data. Risk assessment is central to this type of regulation, known as risk regulation, in which controls for technological hazards are adapted to their probabilities, and defined before they occur (Hood et al. 2001, Shapiro and Glicksman 2003, Fisher 2009, Demortain 2020). It took form progressively in the 1970s and 1980s thanks to the contributions of a series of disciplines such as actuarial sciences, geography and natural disaster research, physics, operational research, all of which converged towards the notion that the probability of hazards could be computed, and that such calculations would help anticipate and mitigate hazards. Prediction has been one of the rallying calls for the scientists that united to give birth to the interdisciplinary movement of risk assessment (Rip 1986, Jasanoff 1990, Demortain 2020). Several decades later, the broad movement of digitalization and the promises of artificial intelligence seems to be pushing the limits of risk assessment, and heralds an era of faster and more precise predictions. The conversion of more diverse and larger sets of information into storable, classifiable and analyzable digital forms, and the design and adoption of IT technologies allowing organizations to perform such tasks at a quick pace and minimal cost, revives the ambitions of risk assessors to anticipate risks (Hutter 2010). And indeed, most of risk assessment practitioners have joined the call to accelerate and deepen the movement of digitalization, embracing like many other sciences the age of big data (Kitchin 2014). The imaginary of continuous, non-human-mediated production of data to train and feed predictive machines (Benbouzid et Cardon 2018), and quickly discover new cause-and-effect relationships in complex systems, has penetrated risk assessment and risk analysis (Aven and Flage 2020). This short paper reviews the history of chemical risk assessment methods developed by regulatory bodies and associated research groups, and the complex ways in which the predictive has transformed risk assessment sciences — among which toxicology and biochemistry. It identifies what it is that is currently happening in this area, and to better determine whether the ever-revamped technological promise of prediction is within a closer reach than it was before.