Zero-risk bias is a tendency to prefer the complete elimination of a risk even when alternative options produce a greater reduction in risk (overall). This effect on decision making has been observed in surveys presenting hypothetical scenarios and certain real-world policies (e.g. war against terrorism as opposed to reducing the risk of traffic accidents or gun violence) have been interpreted as being influenced by it.
Scientists identified a zero-risk bias in responses to a questionnaire about a hypothetical cleanup scenario involving two hazardous sites X and Y, with X causing 8 cases of cancer annually and Y causing 4 cases annually. The respondents ranked three cleanup approaches: two options each reduced the total number of cancer cases by 6, while the third reduced the number by 5 and completely eliminated the cases at site Y. While the latter option featured the worst reduction overall, 42% of the respondents ranked it better than at least one of the other options. This conclusion resembled one from an earlier economics study that found people were willing to pay high costs to completely eliminate a risk.
Multiple real-world policies have been said to be affected by this bias. In American federal policy, the Delaney clause outlawing cancer-causing additives from foods (regardless of actual risk) and the desire for perfect cleanup of Superfund sites have been alleged to be overly focused on complete elimination. Furthermore, the effort needed to implement zero-risk laws grew as technological advances enabled the detection of smaller quantities of hazardous substances. Limited resources were increasingly being devoted to low-risk issues.
Other biases might underlie the zero-risk bias. One is a tendency to think in terms of proportions rather than differences. A greater reduction in proportion of deaths is valued higher than a greater reduction in actual deaths. The zero-risk bias could then be seen as the extreme end of a broad bias about quantities as applied to risk. Framing effects can enhance the bias, for example, by emphasizing a large proportion in a small set or can attempt to mitigate the bias by emphasizing total quantities.