Non-epistemic Values Research Articles

Practical pursuit in stem cell biology: Innovation, translation, and incomplete theorization

This paper aims to contribute to the study of practical pursuit-worthiness in science by engaging with a case in therapeutic stem cell biology. Induced pluripotent stem cell (iPSC) research emerged from research in developmental biology and the molecular biology of cell fate conversion. It took on practical significance when proposed as an alternative to therapeutic stem cell research that used human embryonic stem cells. The supposed ability of iPSC research to tackle ethical and regulatory constraints on research at the beginning of the twentieth century was a central part of the heuristic assessment of iPSC. However, the development and transfer of knowledge from experimental and theoretical biology to preclinical pursuit conflicted with the framing of biomedical innovation in public policy. The framing of innovation operated as part of the heuristic assessment of the pursuit-worthiness of iPSCs in the United States and was characterized by attempts to underdetermine conflicting ethical and socio-economic values – to seek innovations that are “incompletely theorized” in the sense that they purportedly allow stakeholders to refrain from engagement with the divisive values that created impediments to research in stem cell biology. When conflict arose with the epistemic standards in preclinical pursuit required to ensure the safety and efficacy of biomedical innovations, it resulted in the critical appraisal of the values used to rationalize policies for the distribution of federal resources for biomedical research. The case demonstrates how non-epistemic values impinge on standards of assessment in translational science, how background assumptions about innovation can drive practical pursuit, and how conflicting values and goals in research creates an important context for the appraisal of emerging science, technology and policy.

Climate tipping points and expert judgment

AbstractExpert judgment can be seen throughout climate science and even more prominently when discussing climate tipping points. To provide an accurate characterization of expert judgment we begin by evaluating the existing literature on expertise as it relates to climate science as a whole, before then focusing the literature review on the role of expert judgment in the unique context of climate tipping points. From this we turn our attention to the structured expert elicitation protocols specifically developed for producing expert judgments about tipping points. We highlight that expert elicitation is not only used for the quantification of uncertainty in this context, but also for the very identification and characterization of tipping points and their interactions, making expert judgment in itself a genuine scientific output. The central role of expert judgment in this domain raises several epistemic issues that require careful attention. Among other topics, we discuss the relationship between expert judgment and modeling, as well as the nonepistemic values that are involved in the production of expert judgments, highlighting how the elicitation protocols can be used to manage these values. In the perspective of climate change, clarifying the epistemic foundations of expert judgment in this context can help to navigate the epistemic situation between self‐defeating alarmism and blind dismissal, thus contributing to a better understanding of the challenges related to climate (and Earth system) tipping points.This article is categorized under:Climate, History, Society, Culture > Ideas and KnowledgeThe Social Status of Climate Change Knowledge > Climate Science and Decision Making

No free theory choice from machine learning

Ravit Dotan argues that a No Free Lunch theorem (NFL) from machine learning shows epistemic values are insufficient for deciding the truth of scientific hypotheses. She argues that NFL shows that the best case accuracy of scientific hypotheses is no more than chance. Since accuracy underpins every epistemic value, non-epistemic values are needed to assess the truth of scientific hypotheses. However, NFL cannot be coherently applied to the problem of theory choice. The NFL theorem Dotan’s argument relies upon is a member of a family of theorems in search, optimization, and machine learning. They all claim to show that if no assumptions are made about a search or optimization problem or learning situation, then the best case performance of an algorithm is that of random search or random guessing. A closer inspection shows that these theorems all rely upon assigning uniform probabilities over problems or learning situations, which is just the Principle of Indifference. A counterexample can be crafted that shows that NFL cannot be coherently applied across different descriptions of the same learning situation. To avoid this counterexample, Dotan needs to privilege some description of the learning situation faced by scientists. However, this means that NFL cannot be applied since an important assumption about the problem is being made. So Dotan faces a dilemma: either NFL leads to incoherent best-case partial beliefs or it is inapplicable to the problem of theory choice. This negative result has implications for the larger debate over theory choice.

How do value-judgements enter model-based assessments of climate sensitivity?

Philosophers argue that many choices in science are influenced by values or have value-implications, ranging from the preference for some research method’s qualities to ethical estimation of the consequences of error. Based on the argument that awareness of values in the scientific process is a necessary first step to both avoid bias and attune science best to the needs of society, an analysis of the role of values in the physical climate science production process is provided. Model-based assessment of climate sensitivity is taken as an illustrative example; climate sensitivity is useful here because of its key role in climate science and relevance for policy, by having been the subject of several assessments over the past decades including a recent shift in assessment method, and because it enables insights that apply to numerous other aspects of climate science. It is found that value-judgements are relevant at every step of the model-based assessment process, with a differentiated role of non-epistemic values across the steps, impacting the assessment in various ways. Scrutiny of current philosophical norms for value-management highlights the need for those norms to be re-worked for broader applicability to climate science. Recent development in climate science turning away from direct use of models for climate sensitivity assessment also gives the opportunity to start investigating the role of values in alternative assessment methods, highlighting similarities and differences in terms of the role of values that encourage further study.

Enhanced Epistemic Trust and the Value-Free Ideal as a Social Indicator of Trust

ABSTRACT 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.

Transparency, Values and Trust in Science

Current debates over inductive risk and the role(s) of values in science have largely revolved around the question of the moral responsibilities of scientists: Do scientists have the duty to consider the potential non-epistemic consequences of theories they advocate and, if yes, what (or whose) values should be taken into account in decision-making? The paper discusses two different – though potentially complementary – responses to this question: a) H. Douglas’s view that scientists should avoid causing reckless or negligent harm to others as a result of the decisions they make and b) K. Elliott’s Multiple Goals Criterion. Drawing from the case of potential coronavirus transmission by sharing the Holy Communion that recently divided Greek society and medical experts, it shows the tensions emerging between autonomy and the moral responsibilities of scientists, when the boundaries of science are blurred and the epistemic goal of truth is inconsistent with (or succumbs to) alternative goals. It argues that the balance attempted between scientific principles and religious beliefs was unattainable and concludes that the need to distinguish between epistemic and non-epistemic values (and contexts or truths), which is traditionally related to the ideal of value free science, should be reconsidered and even prioritized among the responsibilities of scientists.

Inductive risk: does it really refute value-freedom?

The argument from inductive risk is considered to be one of the strongest challenges for value-free science. A great part of its appeal lies in the idea that even an ideal epistemic agent—the “perfect scientist” or “scientist qua scientist”—cannot escape inductive risk. In this paper, I scrutinize this ambition by stipulating an idealized Bayesian decision setting. I argue that inductive risk does not show that the “perfect scientist” must, descriptively speaking, make non-epistemic value-judgements, at least not in a way that undermines the value-free ideal. However, the argument is more successful in showing that there are cases where the “perfect scientist” should, normatively speaking, use non-epistemic values. I also show that this is possible without creating problems of illegitimate prescription and wishful thinking. Thus, while inductive risk does not refute value-freedom completely, it still represents a powerful critique of value-free science.

Inductive Risk, Understanding, and Opaque Machine Learning Models

AbstractUnder what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes.

Epistemic Priority or Aims of Research? A Critique of Lexical Priority of Truth in Regulatory Science

A general criterion for distinguishing between epistemic and non-epistemic values is that the former promotes the attainment of truth whereas the latter does not. Daniel Steel (2010, 2016) is a proponent of this criterion, although it was initially proposed by McMullin (1983). There are at least two consequences of this criterion; (i) it always prioritizes epistemic values over non-epistemic values in scientific research, and (ii) it overlooks the diverse aims of science, especially the aims of regulatory or policy-oriented science. This criterion assumes the lexical priority of truth or lexical priority of evidence. This paper attempts to show a few inadequacies of this assumption. The paper also demonstrates why epistemic priority over non-epistemic values is a problematic stance and how constraining the role of non-epistemic values as ‘tiebreakers’ may undermine the diverse aims of science.

Non-epistemic values and scientific assessment: an adequacy-for-purpose view

The literature on values in science struggles with questions about how to describe and manage the role of values in scientific research. We argue that progress can be made by shifting this literature’s current emphasis. Rather than arguing about how non-epistemic values can or should figure into scientific assessment, we suggest analyzing how scientific assessment can accommodate non-epistemic values. For scientific assessment to do so, it arguably needs to incorporate goals that have been traditionally characterized as non-epistemic. Building on this insight, we show how the adequacy-for-purpose framework recently developed for assessing scientific models can provide a general framework for describing scientific assessment so that it goes beyond purely epistemic considerations. Adopting this framework has significant advantages and opens the possibility of effecting a partial rapprochement between critics and proponents of the value-free ideal.

That Obscure Concept of Desire: The Ideological War over Scientific Models

AbstractThis article analyzes the scientific debate surrounding the appropriateness of pharmacological interventions aimed at increasing female desire. It has three objectives: (1) to outline the nonepistemic values that underlie scientific concepts of desire; (2) to conduct an analysis of scientific misuses when justifying a drive-based model of desire and pharmacological intervention; and (3) to show how in this and similar cases of vague constructs we can demonstrate the role of nonepistemic values and assess their influence.

The Limits of Democratizing Science: When Scientists Should Ignore the Public

AbstractScientists are frequently called upon to “democratize” science, by bringing the public into scientific research. One appealing point for public involvement concerns the nonepistemic values involved in science. Suppose, though, a scientist invites the public to participate in making such value-laden determinations but finds that the public holds values the scientist considers morally unacceptable. Does the argument for democratizing science commit the scientist to accepting the public’s objectionable values, or may she veto them? I argue that there are a limited set of cases in which scientists can, consistently with a commitment to democratized science, set aside the public’s judgments.

Prior Information in Frequentist Research Designs: The Case of Neyman’s Sampling Theory

We analyse the issue of using prior information in frequentist statistical inference. For that purpose, we scrutinise different kinds of sampling designs in Jerzy Neyman’s theory to reveal a variety of ways to explicitly and objectively engage with prior information. Further, we turn to the debate on sampling paradigms (design-based vs. model-based approaches) to argue that Neyman’s theory supports an argument for the intermediate approach in the frequentism vs. Bayesianism debate. We also demonstrate that Neyman’s theory, by allowing non-epistemic values to influence evidence collection and formulation of statistical conclusions, does not compromise the epistemic reliability of the procedures and may improve it. This undermines the value-free ideal of scientific inference.

Values in Science, Biodiversity Research, and the Problem of Particularity

How to deal with non-epistemic values in science presents a pressing problem for science and society as well as for philosophers of science. In recent years, accounts of democratizing science have been proposed as a possible solution to this. By providing a case study on the establishment of the Intergovernmental Science-Policy comment: Platform on Biodiversity and Ecosystem Services comment: (IPBES), I argue that such accounts run into a problem when values are embedded in the general scientific and societal setup to such an extent that they shape the terrain upon which such a democratization needs to take place. I introduce the notion of particularities as manifestations of values in science and state a problem of particularity, posed by the ways in which the interactive dimension of particularities interferes with democratic procedures for resolving value judgements in science. As a possible remedy, I propose enriching accounts of democratizing science by agonistic theories of democracy.

When do non-epistemic values play an epistemically illegitimate role in science? How to solve one half of the new demarcation problem

Solving the “new demarcation problem” requires a distinction between epistemically legitimate and illegitimate roles for non-epistemic values in science. This paper addresses one ‘half’ (i.e. a sub-problem) of the new demarcation problem articulated by the Gretchenfrage: What makes the role of a non-epistemic value in science epistemically illegitimate? I will argue for the Explaining Epistemic Errors (EEE) account, according to which the epistemically illegitimate role of a non-epistemic value is defined via an explanatory claim: the fact that an epistemic agent is motivated by a non-epistemic value explains why the epistemic agent commits a particular epistemic error. The EEE account is inspired by Douglas’ and Steel’s “functionalist” or “epistemic constraint” accounts of epistemic illegitimacy. I will suggest that the EEE account is able to meet two challenges that these two accounts face, while preserving the key intuition underlying both accounts. If my arguments succeed, then the EEE account provides a solution to one half of the new demarcation problem (by providing a definition of what makes the role of a non-epistemic value epistemically illegitimate) and it opens up new ways for addressing the other half (i.e. characterizing an epistemically legitimate role for non-epistemic values).

Policy Styles and Epistemic Policies in the Regulation of Health Claims. A Comparison of Europe, the United States, and Japan

ABSTRACT I analyse epistemic and non-epistemic factors involved in expert advisory practices and decision-making relating to the regulation of health claims in the US, EU, and Japan. I consider the changes that have taken place, historically, in regulatory policies in all three cases in order to confirm the hypothesis that not only epistemic, but also non-epistemic factors and values determine the methodological decisions on which expert assessments are based for the authorization of health claims. I found that the current European, US, and Japanese assessment systems are based on different kinds of what have been termed ‘epistemic policies’ and that these policies and other characteristics shape what might be identified as differing ‘policy styles’ in the regulation of health claims.

How to Incorporate Non-Epistemic Values into a Theory of Classification

Non-epistemic values play important roles in classificatory practice, such that philosophical accounts of kinds and classification should be able to accommodate them. Available accounts fail to do so, however. Our aim is to fill this lacuna by showing how non-epistemic values feature in scientific classification, and how they can be incorporated into a philosophical theory of classification and kinds. To achieve this, we present a novel account of kinds and classification (the Grounded Functionality Account), discuss examples from biological classification where non-epistemic values play decisive roles, and show how this account accommodates the role of non-epistemic values.

Is poor mental health an unrecognised occupational health and safety hazard for conservation biologists and ecologists? Reported incidences, likely causes and possible solutions

Workers in many professions suffer from poor mental health as a result of their employment. Although a bibliographic search generated little published evidence for poor mental health among conservation biologists and ecologists, the phenomenon has been reported among researchers working on coral reefs, climate change, wildfires and threatened species. Factors responsible for poor mental health include (1) epistemic attributes associated with conservation biologists’ and ecologists’ deep knowledge base; (2) non-epistemic values associated with their view of the natural world; and (3) a complex suite of factors relating to the wider social, political and economic milieu in which they practise their trade. Because it relates directly to employment, poor mental health among conservation biologists and ecologists must be differentiated from the phenomena of ‘environmental grief’ and ‘solastalgia’ reported in the wider community. A number of solutions to the problem have been suggested, including appreciating the conservation successes that have been achieved, recognising the importance of collegiality and comradeship, acknowledging the role of grieving rituals, active intervention via therapeutic counselling, reducing the incidence of censorship and repression of scientists’ research, and the adoption of a Stoic view of the world. I propose a different approach: conservation biologists and ecologists should reposition their personal experiences within an historical perspective that sees them as part of a long tradition of struggle to protect the natural environment. An apt rallying cry to help conservation biologists and ecologists manage their mental health is Pablo Casals’ ‘The situation is hopeless. We must take the next step’.

Distinguishing between legitimate and illegitimate roles for values in transdisciplinary research

In this paper, we argue that the new demarcation problem does not need to be framed as the problem of defining a set of necessary and jointly sufficient criteria for distinguishing between acceptable and unacceptable roles that non-epistemic values can play in science. We introduce an alternative way of framing the problem and defend an open-ended list of criteria that can be used in demarcation. Applying such criteria requires context-specific work that clarifies which principles should be used, and possibly leads to the identification of new principles – which then can be added to the open-ended list. We illustrate our approach by examining a context where distinguishing between acceptable and unacceptable value influences in science is both needed and tricky: transdisciplinary research.

What Does Good Science-Based Advice to Politics Look Like?

I address options for providing scientific policy advice and explore the relation between scientific knowledge and political, economic and moral values. I argue that such nonepistemic values are essential for establishing the significance of questions and the relevance of evidence, while, on the other hand, such social choices are the prerogative of society. This tension can be resolved by recognizing social values and identifying them as separate premises or as commissions while withholding commitment to them, and by elaborating a plurality of policy packages that envisage the implementation of different social goals. There are limits to upholding the value-free ideal in scientific research. But by following the mentioned strategy, science can give useful policy advice by leaving the value-free ideal largely intact. Such scientific restraint avoids the risk of appearing to illegitimately impose values on the public and could make the advice given more trustworthy.