The proper role of non-epistemic values such as moral, political, and social values in practices of justification of policy-relevant hypotheses has recently become one of the central questions in philosophy of science. This strand of research has yielded conceptual clarifications and significant insight into the complex and notoriously contentious issue of the proper relationship between science, non-epistemic values, and policymaking. A central part of this discussion revolves around whether scientists should aspire for the value-free ideal, according to which non-epistemic values (...) are unacceptable in the justification of hypotheses. By so doing, scientists can... (shrink)

Drawing on the SAGE minutes and other documents, I consider the wider lessons for norms of scientific advising that can be learned from the UK’s initial response to coronavirus in the period January-March 2020, when an initial strategy that planned to avoid total suppression of transmission was abruptly replaced by an aggressive suppression strategy. I introduce a distinction between “normatively light advice”, in which no specific policy option is recommended, and “normatively heavy advice” that does make an explicit recommendation. I (...) argue that, although scientific advisers should avoid normatively heavy advice in normal times in order to facilitate democratic accountability, this norm can be permissibly overridden in situations of grave emergency. SAGE’s major mistake in early 2020 was not that of endorsing a particular strategy, nor that of being insufficiently precautionary, but that of relying too heavily on a specific set of “reasonable worst-case” planning assumptions. I formulate some proposals that assign a more circumscribed role to “worst-case” thinking in emergency planning. In an epilogue, I consider what the implications of my proposals would have been for the UK’s response to the “second wave” of late 2020. (shrink)

Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is “cognitive-motor dissociation” (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram (EEG) recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a procedure (...) for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) a shift from categorical “responding or not” assessments to degrees of evidence; (2) the use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) the use of standardized probability yardsticks to convey those assessments as clearly as possible. (shrink)