Article (Scientific journals)
‘A mechanistic interpretation, if possible’: How does predictive modelling causality affect the regulation of chemicals?
Thoreau, François
2016In Big Data and Society, July-December (1), p. 1-11
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Keywords :
Predictive modelling; quantitative structure–activity relationship; regulation
Abstract :
[en] 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.
Research center :
Spiral
Disciplines :
Sociology & social sciences
Author, co-author :
Thoreau, François  ;  Université de Liège > Département de philosophie > Philosophie morale et politique
Language :
English
Title :
‘A mechanistic interpretation, if possible’: How does predictive modelling causality affect the regulation of chemicals?
Publication date :
2016
Journal title :
Big Data and Society
eISSN :
2053-9517
Publisher :
SAGE
Volume :
July-December
Issue :
1
Pages :
1-11
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 23 September 2016

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