The aim of this article is to question the epistemic presuppositions of applying behavioural science in public policymaking. Philosophers of science who have examined the recent applications of the behavioural sciences to policy have contributed to discussions on causation, evidence, and randomised controlled trials. These have focused on epistemological and methodological questions about the reliability of scientific evidence and the conditions under which we can predict that a policy informed by behavioural research will achieve the policymakers’ goals. This paper argues (...) that the philosophical work of Helen Longino can also help us to have a better and fuller understanding of the knowledge which the behavioural sciences provide. The paper advances an analysis of the knowledge claims that are made in the context of policy applications of behavioural science and compares them with the behavioural research on which they are based. This allows us to show that behavioural policy and the debates accompanying it are based on an oversimplified understanding of what knowledge behavioural science actually provides. Recognising this problem is important as arguments that justify reliance on the behavioural sciences in policy typically presume this simplification. (shrink)

When discussing scientific objectivity, many philosophers of science have recently focused on accounts that can be applied in practice when assessing the objectivity of something. It has become clear that in different contexts, objectivity is realized in different ways, and the many senses of objectivity recognized in the recent literature seem to be conceptually distinct. I argue that these diverse ‘applicable’ senses of scientific objectivity have more in common than has thus far been recognized. I combine arguments from philosophical discussions (...) of trust, from negative accounts of objectivity, and from the recent literature on epistemic risks. When we call X objective, we endorse it: we say that we rely on X, and that others should do so too. But the word ‘objective’ is reserved for a specific type of reliance: it is based on the belief that important epistemic risks arising from our imperfections as epistemic agents have been effectively averted. All the positive senses of objectivity identify either some risk of this type, or some efficient strategy for averting one or more such risks. (shrink)

In our interactions with science, we are often vulnerable; we do not have complete control of the situation and there is a risk that we, or those we love, might be harmed. This is not an emotionall...

Both scientists and philosophers of science have recently emphasized the importance of promoting transparency in science. For scientists, transparency is a way to promote reproducibility, progress, and trust in research. For philosophers of science, transparency can help address the value-ladenness of scientific research in a responsible way. Nevertheless, the concept of transparency is a complex one. Scientists can be transparent about many different things, for many different reasons, on behalf of many different stakeholders. This paper proposes a taxonomy that clarifies (...) the major dimensions along which approaches to transparency can vary. By doing so, it provides several insights that philosophers and other science studies scholars can pursue. In particular, it helps address common objections to pursuing transparency in science, it clarifies major forms of transparency, and it suggests avenues for further research on this topic. (shrink)

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems (...) requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems. (shrink)

. Given the importance of the issue of scientific objectivity in our democratic societies and the significant development of citizen science, it is crucial to investigate how citizen science may either undermine or foster scientific objectivity. This paper identifies a variety of epistemic risks and benefits that participation of lay citizens in scientific inquiries may bring. It also discusses concrete actions and pending issues that should be addressed in order to foster objectivity in citizen science programs.