The setting of evidential standards is a core practice of scientific assessment for policy. Persuaded by considerations of inductive risk, philosophers generally agree that the justification of evidential standards must appeal to non-epistemic values but debate whether the balance of non-epistemic reasons favours varying evidential standards versus maintaining fixed high evidential standards in assessment, as both sets of standards promote different and important political virtues of advisory institutions. In this paper, I adjudicate the evidential standards debate by developing a novel (...) argument from justice, drawing on the IPCC’s assessment of climate impacts as a case study. I argue that in assessments marked by background evidential inequality, maintaining fixed high evidential standards results in an unequal distribution of ‘epistemic power’ among stakeholders, producing a ‘powerful assessment’ for the data-rich (a high rate of findings) and a ‘weak assessment’ for the data-poor (a low rate of findings). Where such inequalities of epistemic power disadvantage those in data-poor regions with respect to fundamental interests, such as basic human rights, we have decisive reasons of justice to vary evidential standards. (shrink)

When faced with an urgent and credible threat of grave harm, we should take proportionate precautions. This maxim captures the core commitments of the “precautionary principle”. But what is it for a precaution to be “proportionate”? I construct an account of proportionality (the “ARCANE” account) that consists of five fundamental conditions (absolute rights compatibility, reasonable compensation, consistency, adequacy and non- excessiveness) and a tie-breaker (efficiency). I apply this account to two examples from the COVID-19 pandemic (border closures and school closures), (...) arguing that my account captures the key questions on which it is both feasible and important to integrate expert input with democratic input. I close by considering how we might try to manage the risk of future pandemics in a proportionate way. (shrink)