'A mechanistic interpretation, if possible': How does predictive modelling causality affect the regulation of chemicals?. Issue 2 (September 2016)

• Record Type: Journal Article
• Authors: Thoreau, François
• Is Part Of: Big data & society. Volume 3:Issue 2(2016)
• Publication Date: 2016-09
• Subjects: Predictive modelling -- quantitative structure–activity relationship -- causality -- risk regulation; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/2053951716670189
• Languages: English
• ISSNs: 2053-9517

Abstract

The regulation of chemicals is undergoing drastic changes with the use of computational models to predict environmental toxicity. This particular issue has not attracted much attention, despite its major impacts on the regulation of chemicals. This raises the problem of causality at the crossroads between data and regulatory sciences, particularly in the case models known as quantitative structure–activity relationship models. This paper shows that models establish correlations and not scientific facts, and it engages anew the way regulators deal with uncertainties. It does so by exploring the tension and problems raised by the possibility of causal explanation afforded by quantitative structure–activity relationship models. It argues that the specificity of predictive modelling promotes rethinking of the regulation of chemicals.