there was little or no scientific basis for discriminating among the range of assumptions or models that might be used in a given case. Given that situation, risk assessments were not likely to achieve any degree of consistency and, indeed, might be easily “tailored” to meet any risk-management objective. The report argued that some degree of general scientific understanding, though limited, exists in each of the areas of uncertainty that attend risk assessment. It further argued that, in many of the areas of uncertainty, a range of plausible scientific inferences might be made, although none could be claimed to be generally correct (that is, correct for all or most specific cases). If the agencies conducting risk assessments could select, for each step where one was needed, the “best supported” option or inference and could apply that inference to all of its risk assessments, then it could be possible to be consistent in risk assessment and to minimize case-by-case manipulations. Determining the “best” option cannot be based upon science alone, but also requires a policy choice, and agencies needed to specify clearly the scientific and policy bases for their choices among available options. The report further stated that the selected set of inference options for risk assessment should not only be justified, but also be set down in written guidelines for the conduct of risk assessments, so that they could be visible to all (NRC, 1983).

As recommended, EPA has developed guidelines for the conduct of risk assessments for many types of adverse effects, and those guidelines include recommendations about what uncertainty factors to use when there are specific uncertainties (EPA, 1986, 1992, 1997a,b,d, 1998a, 2004, 2005a). The selected sets of inference options have come to be called uncertainty factors, or defaults. In practice, in reviewing the scientific information available on specific substances or exposures, it becomes clear that there are significant gaps in knowledge or information; agency human health risk assessors adopt the relevant default specified in the guidelines. For example, to account for uncertainties in how to extrapolate from animal data to risks in humans, the default uncertainty factor is 10. EPA, therefore, divides the dose at which no effect is seen in animals by a factor of 10 to estimate a dose at which an effect would not be seen in humans. If there are data on the extent of toxicokinetic differences between animals and humans, then EPA might use a data-derived uncertainty factor rather than using the default uncertainty factor.

The Problems with Default-Driven Risk Assessments

In addition to helping make risk assessments consistent across agencies, the use of prespecified, generic defaults has a number of advantages. First, although the uncertainties and limitations in the estimate should be characterized for the decision maker, the use of a default does allow the



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