This review examines the alleged crisis of trust in environmental science and its impact on public opinion, policy decisions in the context of democratic governance, and the interaction between science and society. In an interdisciplinary manner, the review focuses on the following themes: the trustworthiness of environmental science, empirical studies of levels of trust and trust formation; social media, environmental science, and disinformation; trust in environmental governance and democracy; and co-production of knowledge and the production of trust in knowledge. The review explores both the normative issue of trustworthiness and empirical studies on how to build trust. The review does not provide any simple answers to whether trust in science is generally in decline or whether we are returning to a less enlightened era in public life with decreased appreciation of knowledge and truth. The findings are more nuanced, showing signs of both distrust and trust in environmental science. Expected final online publication date for the Annual Review of Environment and Resources, Volume 47 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The role of scientists as experts is crucial to public policymaking. However, the expert role is contested and unsettled in both public and scholarly discourse. In this paper, I provide a systematic account of the role of scientists as experts in policymaking by examining whether there are any normatively relevant differences between this role and the role of scientists as researchers. Two different interpretations can be given of how the two roles relate to each other. The separability view states that there is a normatively relevant difference between the two roles, whereas the inseparability view denies that there is such a difference. Based on a systematic analysis of the central aspects of the role of scientists as experts - that is, its aim, context, mode of output, and standards, I propose a moderate version of the separability view. Whereas the aim of scientific research is typically to produce new knowledge through the use of scientific method for evaluation and dissemination in internal settings, the aim of the expert is to provide policymakers and the public with relevant and applicable knowledge that can premise political reasoning and deliberation.

ObjectiveTo demonstrate what it takes to reconcile the idea of fairness in medical algorithms and machine learning (ML) with the broader discourse of fairness and health equality in health research.MethodThe methodological approach used in this paper is theoretical and ethical analysis.ResultWe show that the question of ensuring comprehensive ML fairness is interrelated to three quandaries and one dilemma.DiscussionAs fairness in ML depends on a nexus of inherent justice and fairness concerns embedded in health research, a comprehensive conceptualisation is called for to make the notion useful.ConclusionThis paper demonstrates that more analytical work is needed to conceptualise fairness in ML so it adequately reflects the complexity of justice and fairness concerns within the field of health research.

This article contributes to the philosophical debate on values in science by exploring how scientists themselves understand the proper role of moral, political, and social values in expert practice. I present findings from interviews with climate scientists who have participated as authors in the Intergovernmental Panel on Climate Change (IPCC). The climate scientists subscribe to the value-free ideal as a regulative ideal that applies both to the provision of knowledge to policymakers and how they engage with political issues in the public sphere. Yet their views on the moral responsibility of scientists and the aim of providing policy-relevant output challenge the value-free ideal. The article suggests ways in which their views can be relevant to the philosophical discussion.