Premium
Regulatory False Positives: True, False, or Uncertain?
Author(s) -
Cox Louis Anthony
Publication year - 2007
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2007.00975.x
Subject(s) - false positive paradox , harm , precautionary principle , false positives and false negatives , set (abstract data type) , false belief , categorization , risk analysis (engineering) , computer science , medicine , psychology , social psychology , artificial intelligence , psychiatry , ecology , cognition , theory of mind , biology , programming language
Hansen et al. (2007) recently assessed the historical performance of the precautionary principle in 88 specific cases, concluding that “applying our definition of a regulatory false positive, we were able to identify only four cases that fit the definition of a false positive.” Empirically evaluating how prone the precautionary principle is to classify nonproblems as problems (“false positives”) is an excellent idea. Yet, Hansen et al. 's implementation of this idea applies a diverse set of questionable criteria to label many highly uncertain risks as “real” even when no real or potential harm has actually been demonstrated. Examples include treating each of the following as reasons to categorize risks as “real”: considering that a company's actions contaminated its own product; lack of a known exposure threshold for health effects; occurrence of a threat; treating deliberately conservative (upper‐bound) regulatory assumptions as if they were true values; treating assumed exposures of children to contaminated soils (by ingestion) as evidence that feared dioxin risks are real; and treating claimed (sometimes ambiguous) epidemiological associations as if they were known to be true causal relations. Such criteria can classify even nonexistent and unknown risks as “real,” providing an alternative possible explanation for why the authors failed to find more false positives, even if they exist.