T.Y. Branch

Replacing the value-free ideal (VFI) for science requires attention to the broader understanding of how science in society should function. In public spaces, science needed to project the VFI in norms for science advising, science education, and science communication. This resulted in the independent science advisor model and a focus on science literacy for science education and communication. Attending to these broader implications of the VFI which structure science and society relationships is crucial if we are to properly replace the VFI with a better ideal.

Publics trust experts for personal and pro-social reasons. Scientists are among the experts publics trust most, and so, epistemic trust is routinely afforded to them. The call for epistemic trust to be more socially situated in order to account for the impact of science on society and public welfare is at the forefront of enhanced epistemic trust. I argue that the value-free ideal for science challenges establishing enhanced epistemic trust by preventing the inclusion of non-epistemic values throughout the evaluation of evidence and communication of these values. By selectively silencing non-epistemic values, the ideal cannot take into account publics’ social and moral responses to inductive risk, which are instrumental for defining and determining public welfare. Furthermore, by emphasising epistemic values almost exclusively in science education and communication, the value-free ideal is presented to publics in such a way that it becomes a social indicator of trust. I show this through examination of the importance of values in decisions to trust, and conclude that values (and restrictions on them) can be used by lay publics to help decide which experts to trust.

Expert knowledge regularly informs personal and civic-decision making. To decide which experts to trust, lay publics —including policymakers and experts from other domains—use different epistemic and non-epistemic cues. Epistemic cues such as honesty, like when experts are forthcoming about conflicts of interest, are a popular way of understanding how people evaluate and decide which experts to trust. However, many other epistemic cues, like the evidence supporting information from experts, are inaccessible to lay publics. Therefore, lay publics simultaneously use second-order social cues in their environment to inform decisions to trust. These second-order social cues, or ‘social indicators of trust’, prevent lay publics from having to trust blindly. Social indicators of trust therefore inform lay publics’ epistemic vigilance, or constant low level-monitoring of testimony from experts. This special issue examines the nature, acquisition and application of social indicators of trust for scientific experts and institutions. It also raises questions about the types of trust asked of lay publics and challenges traditional normative assumptions about the relationship between science and lay publics through study of attitudes, values, and experiences. The issue descriptively re-examines the structure of institutions, their role and methods for ferrying information, as well as how social indicators operate in times of crisis. In this collection of works, we bridge history, science, philosophy of science, science and technology studies, science communication and social epistemology, to broaden the discourse on trust in experts and more accurately reflect the imperfect yet indispensable endeavour that trusting is.

The COVID-19 crisis has highlighted the considerable challenge of sourcing expertise and determining which experts to trust. Dissonant information fostered controversy in public discourse and encouraged an appeal to a wide range of social indicators of trustworthiness in order to decide whom to trust. We analyze public discourse on expertise by examining how social indicators inform the reputation of Dr. Didier Raoult, the French microbiologist who rose to international prominence as an early advocate for using hydroxychloroquine to treat COVID-19. To comprehend how these indicators came to inform his reputation, we outline Dr. Raoult's rise to fame based on discourse about hydroxychloroquine. We then discuss why we trust in experts like scientist-practitioners. This is followed by an examination of how social indicators of trust like status, epistemic authority, influence and values have informed Dr. Raoult's reputation. We conclude with recommendations for how to improve the selection and evaluation social indicators of trust and reputations. Our aim here, instead of making a claim about the efficacy of hydroxychloroquine or Dr. Raoult's reputation per se, is to outline through this case study how social indicators of trust inform reputation and the challenge they present to evaluating expertise.

Democracy hinges on the personal and civic decision-making capabilities of publics. In our increasingly technoscientific world, being well-informed requires an understanding of science. Despite acknowledging public understanding of science as an important part of being well-informed, publics’ engagement with science remains limited. I argue that part of publics’ disengagement with science is because information transmitted about science, like science itself, has been decontextualized. Though there are many ways to decontextualize information, obscuring values in science is a popular means of doing so. Due to the ubiquitous nature of values, science misrepresented as ‘value-free’ will be the focus of my decontextualization critique. Epistemic values and non-epistemic values are the sorts of values that have been misrepresented by views like the value-free ideal. The VFI is the idea that non-epistemic values should not play a role in the evaluation of evidence and has been heavily criticized on practical and normative grounds. This has led to alternatives to the VFI being proposed, including ways for non-epistemic values to be included in the evaluation of evidence. In a yet to be explored implication of the VFI, I argue that models of science education and communication that accommodate the VFI have been popularized as a way to reinforce decontextualization. These models describe science and publics with only a minimal account of values, leading me to challenge them on practical and normative grounds since communicating science as ‘value-free’ is misrepresentative, and from a normative perspective undesirable, especially as including values can help engage publics. In response, I advocate for value-conscious descriptions of science instead. To catalyze this contextualization, I introduce key aspects for understanding values in science and call them the ‘KAUVIS’. By using a basis of transparency between scientists and publics, the KAUVIS provides a means to describe how values interact with science without dictating which values are the correct ones. The KAUVIS includes descriptions of the roles values take on, how values relate to the goals of research, and considers epistemic and ethical values. Hence, the KAUVIS can more accurately represent science than the VFI, and in so doing, contextualizes information about science in relation to research goals and social needs, making science more engaging. By developing the KAUVIS to describe values, I also show that traditional information transmission models are maladapted to conveying the true value- laden nature of science. As a consequence, I examine more value-conscious communication models which I show to be enhanced by the descriptive detail of the KAUVIS. However, unveiling the inner workings of values in science may also have negative consequences for how publics interpret and engage with science. After all, exposing values in science can lead to further dispute about science. Hence, the KAUVIS opens up questions like, what is lost by divulging values in science? My initiatory examination of the drawbacks of being explicit about values will uncover that even though there is a risk that publics may reject scientific claims, an understanding of values in science is desirable for decision-making and deliberation. In other words, a description of values can serve to clarify how they are being used, and help define what it is we are in disagreement about. Thus, by more accurately representing science and values, we might strengthen democracy by better providing publics the contextualized information they need for science to be of service.

This thesis contributes to the growing literature surrounding the importance of weak emergence by showing it can account for more phenomena than originally conceived via the use of computational reduction. Weak emergence refers to unpredictable higher-level phenomena that are reducible to lower-level phenomena. The ability of weak emergence to reduce higher-level phenomena to their lower-level constituents is useful for establishing a mechanistic explanation of emergent features. The tension between holistic higher-level phenomena and lower-level parts is a classic argument in philosophy and investigating emergent phenomena is one means of furthering the discussion. In particular I will look at theories of emergence and computational analysis related to systems biology. Systems biology uses modern computational analysis to model naturally occurring interactions of higher-level elements and, in combination with synthetic biology, can trace complex macro-level interactions back to micro-level occurrences without explanatory loss. What makes this interesting is that systems biology is able to explain and reduce phenomena that were once considered strongly emergent, therefore shrinking the number of phenomena we consider strongly emergent. Furthermore, systems biology offers a compelling milieu for philosophers to contribute to scientific discourse and vice versa. Hence, it is a worthwhile endeavor for philosophers to test their theories of emergence against actual practice as a way to inform their concepts. I will use examples traditionally considered strongly emergent to show that these cases can in fact be reduced and categorized as weakly emergent because they are unpredictable but not irreducible. The reclassification of these phenomena challenges strong emergence, and reveals it to be an ‘Emergence of the Gaps’ whereby the number of phenomena strong emergence can describe decreases in an inversely proportional relationship to increased computational capacity.