The focus of this chapter will be on the epistemic and normative questions at issue in debates about cancer screening, with a special focus on mammography as a case study. Such questions include: How do we know who needs to be screened? What are the benefits and harms of cancer screening, and what is the quality of evidence for each? How ought we to measure and compare these benefits and harms? What are the sources of uncertainty about our estimates of (...) benefit and harm? Why are such issues so contested? What are the major drivers of dissent and consensus on the data and their interpretation? How, if at all, do values play a role in debates surrounding mammography screening? In sum: In what ways does inductive risk, broadly conceived, 1 come into play in the science behind cancer screening, and mammography screening in particular? (shrink)

I examine the construction and evaluation of machine learning binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models (...) is underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk. (shrink)

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can (...) and should be considered in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)

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. (shrink)

The argument from inductive risk has been embraced by many as a successful account of the role of values in science that challenges the value-free ideal. We argue that it is not obvious that the argument from inductive risk actually undermines the value-free ideal. This is because the inductive risk argument endorses an assumption held by proponents of the value-free ideal: that contextual values never play an appropriate role in determining evidence. We show that challenging (...) the value-free ideal ultimately requires rejecting this assumption. (shrink)

This book brings together eleven case studies of inductive risk-the chance that scientific inference is incorrect-that range over a wide variety of scientific contexts and fields. The chapters are designed to illustrate the pervasiveness of inductive risk, assist scientists and policymakers in responding to it, and productively move theoretical discussions of the topic forward.

Built-in decision thresholds for AI diagnostics are ethically problematic, as patients may differ in their attitudes about the risk of false-positive and false-negative results, which will require that clinicians assess patient values.

Cancer—and scientific research on cancer—raises a variety of compelling philosophical questions. This entry will focus on four topics, which philosophers of science have begun to explore and debate. First, scientific classifications of cancer have as yet failed to yield a unified taxonomy. There is a diversity of classificatory schemes for cancer, and while some are hierarchical, others appear to be “cross-cutting,” or non-nested. This literature thus raises a variety of questions about the nature of the disease and disease classification. Second, (...) philosophers of science have historically taken the aim of science to be arriving at true theories. However, scientists studying cancer come from a variety of disciplines, with different scientific as well as practical aims. Perhaps it is not surprising, then, that historians and philosophers of science do not seem to agree on how best to characterize the aim and structure of cancer research; it is far from clear whether the appropriate characterization of the aim is arriving at true theories, or even whether the proper units of analysis are “theories”, or instead, “models”, “explanatory frameworks”, “research programs”, “paradigms”, or perhaps, “experimental traditions”. With the rise of “big data” science—such as the Cancer Genome Atlas Project (or TCGA)—and “systems” approaches to the study of disease, both philosophers and historians of science are rethinking how best to describe and explain these distinctive kinds of scientific inquiry. -/- Third, cancer is in part a byproduct of our developmental and life history, as well as our evolutionary history. Cancer progression can be compared to a reversion of development, or, to the evolution of multicellularity. Thus, cancer raises intriguing questions about how we conceive of “functions”, “development”, and the role of our evolutionary history and particularly, selective trade-offs, in vulnerability to disease. -/- Last but not least, cancer research provides a case study for consideration of the roles of values at the science-policy interface. Epidemiological and toxicological research on cancer’s causes informs toxics law and regulatory policy, which raises a variety of questions about the nature of evidence and inductive risk in such contexts. (shrink)

The use of composite outcomes is becoming widespread in clinical trials. By combining individual outcome measures into a composite, researchers claim a composite can increase statistical precision and trial efficiency, expediting the trial by reducing sample size and cost, and consequently enabling researchers to answer questions that could not otherwise be answered. Another rationale given for using a composite is that it provides a measure of the net effect of the intervention that is more patient-relevant than any single outcome measure. (...) Critics, on the other hand, argue that the use of composites threatens the scientific objectivity of the trial by introducing new risks, and that in practice components are inconsistently defined, unreasonably combined, and inadequately reported. Philosophical scrutiny can help shed light in these disagreements and disentangle ethical and epistemological issues of composites in clinical trials. I examine common use of composites in cardiovascular trials, and show how composite results can be problematic, even misleading, if the proper range of scientific decisions and their consequences are ignored when judging the composite. Philosophies of science that are principled on normative versions of the argument from inductive risk can help explicate issues with composites. I conclude by suggesting ways of remedying composite issues in clinical trials. (shrink)

The argument from inductive risk attempts to show that practical and ethical costs of errors should influence standards of evidence for accepting scientific claims. A common objection charges that this argument presupposes a behavioral theory of acceptance that is inappropriate for science. I respond by showing that the argument from inductive risk is supported by a nonbehavioral theory of acceptance developed by Cohen, which defines acceptance in terms of premising. Moreover, I argue that theories designed to explain how (...) acceptance can be guided exclusively by epistemic values suffer from difficulties that do not afflict Cohen’s theory. (shrink)

과학의 가치중립성은 오랫동안 좋은 과학 활동의 이상으로 간주되어 왔다. 그러나 과학적 추론에 내재한 귀납적 위험과 그에 따르는 실천적 귀결들에 대한 고려는 과학의 가치중립성 이상을 비판하는 논증에서 가장 중요한 근거로 기능해왔다. 과학에서 비인식적 가치의 적법한 역할을 주장하는 “귀납적 위험으로부터의 논증”의 핵심은 다음과 같다. 과학자들은 가설을 수용하거나 거부하는 결정을 내리는데, 그 결정이 잘못되었을 경우 비인식적, 실천적 함의를 가질 수 있기 때문에, 과학자들은 가설을 수용하거나 거부할 때 이러한 가치들을 고려할 필요가 있다. 최근 여러 연구자들은 이러한 귀납적 위험으로부터 논증에 반대하면서, 대안적인 과학의 이상을 제안해왔다. (...) 나는 이 글에서 제프리식 이상과 시지프스식 이상을 통해 대안적인 형태의 가치중립성 이상을 주장하는 철학적 논변들을 검토하고, 어느 쪽도 효과적인 반론을 제시하지 못하였다고 주장한다. 따라서 귀납적 위험으로부터의 논증은 여전히 과학의 가치중립 이상을 비판하는 가장 강력한 논증이다. (shrink)

In recent years, the argument from inductive risk against value free science has enjoyed a revival. This paper investigates and clarifies this argument through means of a case-study: neonicitinoid research. Sect. 1 argues that the argument from inductive risk is best conceptualised as a claim about scientists’ communicative obligations. Sect. 2 then shows why this argument is inapplicable to “public communication”. Sect. 3 outlines non-epistemic reasons why non-epistemic values should not play a role in public communicative contexts. (...) Sect. 4 analyses the implications of these arguments both for the specific case of neonicitinoid research and for understanding the limits of the argument from inductive risk. Sect. 5 sketches the broader implications of my claims for understanding the “Value Free Ideal” for science. (shrink)

Proponents of the value ladenness of science rely primarily on arguments from underdetermination or inductive risk, which share the premise that we should only consider values where the evidence runs out or leaves uncertainty; they adopt a criterion of lexical priority of evidence over values. The motivation behind lexical priority is to avoid reaching conclusions on the basis of wishful thinking rather than good evidence. This is a real concern, however, that giving lexical priority to evidential considerations (...) over values is a mistake and unnecessary for avoiding the wishful thinking. Values have a deeper role to play in science. (shrink)

In this paper I shall defend the idea that there is an abstract and general core meaning of objectivity, and what is seen as a variety of concepts or conceptions of objectivity are in fact criteria of, or means to achieve, objectivity. I shall then discuss the ideal of value-free science and its relation to the objectivity of science; its status can be at best a criterion of, or means for, objectivity. Given this analysis, we can then turn to the (...) problem of inductive risk. Do the value judgements regarding inductive risk really pose a threat to the objectivity of science? I claim that this is not the case because they do not lower the thresholds scientifically postulated for objectivity. I shall conclude the paper with a discussion of under-appreciated influences of values on science, which indeed pose a serious threat to the objectivity of some scientific disciplines. (shrink)

The ‘death of evidence’ issue in Canada raises the spectre of politicized science, and thus the question of what role social values may have in science and how this meshes with objectivity and evidence. I first criticize philosophical accounts that have to separate different steps of research to restrict the influence of social and other non-epistemic values. A prominent account that social values may play a role even in the context of theory acceptance is the argument from (...) inductive risk. It maintains that the more severe the social consequences of erroneously accepting a theory would be, the more evidence is needed before the theory may be accepted. However, an implication of this position is that increasing evidence makes the impact of social values converge to zero; and I argue for a stronger role for social values. On this position, social values may determine a theory’s conditions of adequacy, which among other things can include co.. (shrink)

The argument from inductive risk has become widely accepted as good reason to reject the value-free ideal. The literature that follows is then focused on where inductive risk judgements are required and whose values ought to determine them. The purpose of this paper is twofold. Firstly, to offer motivation for aiming at the value-free ideal, and therefore avoiding inductive risk. To do so I show that there is a tension between principles in science ethics and value (...) encroachment because of inductive risk. Secondly, I offer a renewed defence of Jeffrey’s response to the argument from inductive risk. By appealing to theories in epistemology about rational belief modelling, I argue that the Bayesian belief model offers a suitable alternative to current belief modelling in science, despite criticism that is either explicit in the literature or fairly expected to arise. (shrink)

Arguing from inductive risk, Heather Douglas tried to show that the ideal of value-free science is completely unfounded. The argument has been widely acknowledged to be a strong argument against the ideal. In this paper, beginning with an analysis of the concept of an ideal, we argue that the value-free ideal is an epistemic ideal rather than a practical or ethical ideal. Then, we aim to show that the argument from inductive risk cannot be employed against the value-free (...) ideal as far as it is understood as an epistemic ideal. We try to show that the argument takes practical and ethical limitations of actual scientific enterprise into account to undermine the value-free ideal. But employing non-epistemic considerations makes the argument impotent against an epistemic ideal. (shrink)

The aim of this paper is to address the neglected but important problem of differentiating between epistemically beneficial and epistemically detrimental dissent. By “dissent,” we refer to the act of objecting to a particular conclusion, especially one that is widely held. While dissent in science can clearly be beneficial, there might be some instances of dissent that not only fail to contribute to scientific progress, but actually impede it. Potential examples of this include the tobacco industry’s funding of studies that (...) questioned the link between smoking and lung cancer, and the attempt by the petroleum industry and other groups to cast doubt upon the conclusion that human consumption of fossil fuels contributes to global climate change. The problem of distinguishing between good and bad dissent is important because of the growing tendency of some stakeholders to attempt to delay political action by ’manufacturing doubt’. Our discussion in this paper focuses on climate science. This field, in our view, is rife with instances of bad dissent. On the basis of our discussion of climate science, we articulate a set of sufficient conditions for epistemically problematic dissent in general, which we call “the inductive risk account of epistemically detrimental dissent.”. (shrink)

Critics of the ideal of value‐free science often assume that they must reject the distinction between epistemic and nonepistemic values. I argue that this assumption is mistaken and that the distinction can be used to clarify and defend the argument from inductive risk, which challenges the value‐free ideal. I develop the idea that the characteristic feature of epistemic values is that they promote, either intrinsically or extrinsically, the attainment of truths. This proposal is shown to answer common (...) objections to the distinction and provide a principled basis for separating legitimate from illegitimate influences of nonepistemic values in scientific inference. *Received June 2009; revised September 2009. †To contact the author, please write to: 503 S. Kedzie Hall, Michigan State University, East Lansing, MI 48824‐1032; e‐mail: [email protected]. (shrink)

Under 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. (shrink)

The Argument from Inductive Risk (AIR) is taken to show that values are inevitably involved in making judgements or forming beliefs. After reviewing this conclusion, I pose cases which are prima facie counterexamples: the unreflective application of conventions, use of black-boxed instruments, reliance on opaque algorithms, and unskilled observation reports. These cases are counterexamples to the AIR posed in ethical terms as a matter of personal values. Nevertheless, it need not be understood in those terms. The (...) class='Hi'>values which load a theory choice may be those of institutions or past actors. This means that the challenge of responsibly handling inductive risk is not merely an ethical issue, but is also social, political, and historical. (shrink)

Drug regulation is fraught with inductive risk. Regulators must make a prediction about whether or not an experimental pharmaceutical will be effective and relatively safe when used by typical patients, and such predictions are based on a complex, indeterminate, and incomplete evidential basis. Such inductive risk has important practical consequences. If regulators reject an experimental drug when it in fact has a favourable benefit/harm profile, then a valuable intervention is denied to the public and a company’s material interests (...) are needlessly thwarted. Conversely, if regulators approve an experimental drug when it in fact has an unfavourable benefit/harm profile, then resources are wasted, people are needlessly harmed, and other potentially more effective treatments are underutilized. Given that such regulatory decisions have these practical consequences, non-epistemic values about the relative importance of these consequences impact the way such regulatory decisions are made (similar to the analysis of laboratory studies on the toxic effects of dioxins presented in Douglas [2000]). To balance the competing demands of the pertinent non-epistemic values, regulators must perform what I call an “inductive risk calculus.” At least in the American context this inductive risk calculus is not well-managed. (shrink)

Inmaculada de Melo-Martín and Kristen Intemann consider whether, from the perspective of the argument from inductive risk, ethical and political values might be logically, epistemically, pragmatically, or ethically necessary in the “core” of scientific reasoning. In each case, they argue that there are significant conceptual problems. In this comment, employing regulatory uses of high-throughput toxicology at the US Environmental Protection Agency as a case study, I respond to some of their claims about the notion of “pragmatic necessity.” I (...) conclude that, while an inductive risk framework has some significant limitations, it is still conceptually and rhetorically valuable. (shrink)

How should UNOS deal with the presence of scientific controversies on the risk factors for organ rejection when designing its allocation policies? The answer I defend in this paper is that the more undesirable the consequences of making a mistake in accepting a scientific hypothesis, the higher the degree of confirmation required for its acceptance. I argue that the application of this principle should lead to the rejection of the hypothesis that ‘less than perfect’ Human Leucocyte Antigen (HLA) matches are (...) an important determinant of kidney graft survival. The scientific community has been divided all along on the significance of partial antigen matches. Yet reliance on partial matches has emerged as one of the primary factors leading blacks to spend a much longer time than whites on the waiting list for kidneys, thereby potentially impacting the justice of the kidney allocation policy. My case study illustrates one of the legitimate roles non-epistemic values can play in science and calls into question the ideal of a value-free science. (shrink)

Havstad (2022) argues that the argument from inductive risk for the claim that non-epistemic values have a legitimate role to play in the internal stages of science is deductively valid. She also defends its premises and thus soundness. This is, as far as we are aware, the best reconstruction of the argument from inductive risk in the existing literature. However, there is a small flaw in this reconstruction of the argument from inductive risk which appears to (...) render the argument invalid. This flaw is superficial, and a small amendment to it rescues the claim of validity. (shrink)

I examine how Heather Douglas’ account of values in science applies to the assessment of actual cases of scientific practice. I focus on the case of applied toxicologists’ acceptance of molecular evidence-gathering methods and evidential sources. I demonstrate that a set of social and institutional processes plays a philosophically significant role in changing toxicologists’ inductive risk judgments about different kinds of evidence. I suggest that Douglas’ inductive risk framework can be integrated with a suitable account of evidence, (...) such as Helen Longino’s contextual empiricism, to address the role of social context in the cases like the one examined here. I introduce such an integrated account and show how Longino’s contextual empiricism and inductive risk framework fruitfully complement each other in analyzing the novel aspects of the toxicology case. (shrink)

To clarify the proper role of values in science, focusing on controversial expert responses to Covid-19, this article examines the status of (in)convenient hypotheses. Polarizing cases like health experts downplaying mask efficacy to save resources for healthcare workers, or scientists dismissing “accidental lab leak” hypotheses in view of potential xenophobia, plausibly involve modifying evidential standards for (in)convenient claims. Societies could accept that scientists handle (in)convenient claims just like nonscientists, and give experts less political power. Or societies could hold scientists (...) to a higher bar, by expecting them not to modify evidential standards to avoid costs only incidentally tied to error. (shrink)

The argument from inductive risk (AIR) states that scientists should consider the consequences of hypotheses and methodological choices in the course of ongoing research. It has played a central role in the widespread retreat from the ideal of value-free science. The argument is motivated, to a significant extent, by the laudable concern to use science to better society. I argue that this concern, when taken seriously, tells against the idea that individual working scientists should consider social consequences. First, I (...) show that the AIR assigns scientists a striking task: to frame social decisions and assess their full spectrum of consequences. Then, I argue that scientists should not be assigned such a task. For one thing, if both scientists and policy makers take social consequences into account, we are liable to end up with skewed and biased decisions. For another, scientists are not well-placed to carry out social decision making. I conclude by indicating a context in which the inductive risk line has more force, and by highlighting the importance, in discussing values in science, of attending to units and levels of analysis. (shrink)

This article critically examines a recent philosophical debate on the role of values in climate change forecasts, such as those found in assessment reports of the Intergovernmental Panel on Climate Change. On one side, several philosophers insist that the argument from inductive risk, as developed by Rudner and Douglas among others, applies to this case. AIR aims to show that ethical value judgments should influence decisions about what is sufficient evidence for accepting scientific hypotheses that have implications for (...) policy issues. Advocates of extending AIR to climate science claim that values are deeply... (shrink)

The argument from inductive risk, as developed by Rudner and others, famously concludes that the scientist qua scientist makes value judgments. The paper aims to show that trust in the soundness of the argument is overrated – that philosophers who endorse its conclusion fail to refute two of the most important objections that have been raised to its soundness: Jeffrey’s objection that the genuine task of the scientist is to assign probabilities to hypotheses, and Levi’s objection that the argument (...) is ambiguous about decisions about how to act and decisions about what to believe, that only the former presuppose value judgments, and that qua scientist, the scientist only needs to decide what to believe. (shrink)

This article develops a constructive criticism of methodological conventionalism. Methodological conventionalism asserts that standards of inductive risk ought to be justified in virtue of their ability to facilitate coordination in a research community. On that view, industry bias occurs when conventional methodological standards are violated to foster industry preferences. The underlying account of scientific conventionality, however, is problematically incomplete. Conventions may be justified in virtue of their coordinative functions, but often qualify for posterior empirical criticism as research advances. Accordingly, (...) industry bias does not only threaten existing conventions but may impede their empirically warranted improvement if they align with industry preferences. My empiricist account of standards of inductive risk avoids such a problem by asserting that conventional justification can be pragmatically warranted but has, in principle, only a provisional status. Methodological conventions, therefore, should not only be defended from preference-based infringements on their coordinative function but ought to be subjected to empirical criticism. (shrink)

I argue that non-epistemic values are necessarily embedded in the measure of evidential strength of contrastive evidence. When evidence is contrastive, evidence is stronger the more it favours a hypothesis over a set of plausible, mutually exclusive alternative hypotheses. In such a contrastive epistemic setting, evidence has an effect not only on a particular hypothesis, but on the whole probability distribution over the set of alternative hypotheses. A natural way of analysing the incremental impact of new evidence on a (...) set of alternative hypotheses is in terms of uncertainty or ‘entropy’ reduction. There is no unique single measure of uncertainty/entropy, however, and, consequently, no single unique measure of uncertainty reduction. Finally, I argue that the ‘right’ measure of entropy reduction, and hence of evidential strength, depends on the pragmatic context and non-epistemic values. (shrink)

Philosophical work on values in science is held back by widespread ambiguity about how values bear on sci entific choices. Here, I disambiguate several ways in which a choice can be value-laden and show that this disambiguation has the potential to solve and dissolve philosophical problems about values in science. First, I characterize four ways in which values relate to choices: values can motivate, justify, cause, or be impacted by the choices we make. Next, I (...) put my proposed taxonomy to work, using it to clarify one version of the argument from inductive risk. The claim that non-epistemic values must play a role in scientific choices that run inductive risk makes most sense as a claim about values being needed to justify such choices. The argument from inductive risk is not unique: many philosophical arguments about values in science can be more clearly understood and assessed by paying close attention to how values and choices are related. (shrink)

English abstract: Feminist philosophers of science have argued that various biases can and do influence the results of scientific investigations. Two kinds of arguments have been most influential: On the one hand, it has been argued that biased assumptions frequently bridge the gap between observation and theory associated with ‘the underdetermination thesis’. On the other hand, it has been argued that biased value judgments determine when the evidence in favor of a particular theory is considered sufficiently strong for the theory (...) to be accepted as true. This paper argues that bias can influence the results of scientific investigations via a third route. Briefly, biases can and do influence which theories are seriously proposed and considered in scientific communities, which in turn leads to otherwise promising theories being ignored when scientists are choosing between such theories. In the final section, I suggest that this result speaks in favor of taking a quite radical approach to eliminating unwanted biases in science. (shrink)

The ideal of value free science states that the justification of scientific findings should not be based on non-epistemic (e.g. moral or political) values. It has been criticized on the grounds that scientists have to employ moral judgements in managing inductive risks. The paper seeks to defuse this methodological critique. Allegedly value-laden decisions can be systematically avoided, it argues, by making uncertainties explicit and articulating findings carefully. Such careful uncertainty articulation, understood as a methodological strategy, is exemplified by (...) the current practice of the Intergovernmental Panel on Climate Change (IPCC). (shrink)

Increasingly there are calls for climate services to be “co-produced” with users, taking into account not only the basic information needs of users but also their value systems and decision contexts. What does this mean in practice? One way that user values can be incorporated into climate services is in the management of inductive risk. This involves understanding which errors in climate service products would have particularly negative consequences from the users’ perspective (e.g., underestimating rather than overestimating the (...) change in an impact variable) and then prioritizing the avoidance of those errors. This essay shows how inductive risk could be managed in climate services in ways that serve user values and argues that there are both ethical and practical reasons in favor of doing so. (shrink)

Scientific evidence and scientific values under risk and uncertainty are strictly connected from the point of view of Peirce’s pragmaticism. In addition, economy and statistics play a key role in both choosing and testing hypotheses. Hence we may show also the connection between the methodology of the economy of research and statistical frequentism, both originating from pragmaticism. The connection is drawn by the regulative principles of synechism, tychism and uberty. These principles are values that have both epistemic and (...) non-epistemic dimension. They concern both the decisions to test a hypothesis as well as inductive risk. The validity of this result stems from the values cost–benefit analysis imposes on scientific inquiry. Values associated with the economy of research are important not only in the pre-test phases of generating hypotheses but also when hypotheses are effectively tested. Peirce took these economic considerations to leave room for an interpretation of probability which is not only a frequentist and propensity-theoretic but also a conceptualist one referring to degrees of belief. We show that this leeway nonetheless agrees with the theory of the economy of scientific methods. (shrink)

Should scientists value simple theories? Is fruitfulness an important criterion to assess scientific theories? What role moral, social, and political values should have in the assessment of scientific theories? In recent years, there has been an increasing interest among philosophers of science in studying how cognitive and non-cognitive values influence and should influence the assessment and comparison of scientific theories. While cognitive values (such as simplicity and fruitfulness) are features of scientific theories that are indicative of the (...) truth or empirical adequacy of theories, non-cognitive values are moral, political, social, and economic values. Understanding the roles of values in science is a particularly urgent issue. Clarifying the importance of cognitive values is important in order to be able to make accurate comparisons of scientific theories. Understanding the influence of non-cognitive values on science is crucial because moral, social, and political values are involved in many stages of research, such as decisions on methodologies and allocation of funds. Since these decisions affect all the members of the society (scientists, non-experts, and political institutions), understanding the impact of non-cognitive values on these choices is of primary importance. Although a large body of literature has investigated the roles of cognitive and non-cognitive values in theory appraisal, several questions remain to be analysed. In this dissertation, I address four questions by using two main methods, namely the analysis of case studies from evolutionary psychology and the experimental method. First, while philosophers have clarified the importance and roles in theory appraisal of some cognitive values, little or no attention has been paid to other values. In this dissertation, I have started filling this gap by formulating a clear explication and a simple strategy to be employed in theory appraisal for one of the values that have attracted little attention, namely fruitfulness. I have explicated fruitfulness as the ability of programs to extend their content and suggested considering research questions and discovery heuristics to assess this ability. Moreover, I have used my account to assess the fruitfulness of evolutionary psychology. Second, some philosophers of science argue that cognitive values are desirable and relevant to the assessment of theories because they are indicative of the truth or empirical adequacy of theories. However, why is it so? In my dissertation, I develop a context-sensitive approach to values and I argue that in order to understand the ground for the desirability of cognitive values and make an accurate appraisal of theories, we have to consider specific factors of the context in which theories are assessed, such as the availability of methodologies and the way cognitive values are interrelated to each other in that context. Third, philosophers have traditionally argued that the influence of non-cognitive values on scientific reasoning threatens the epistemic authority of science. I have challenged this view and argued that some non-cognitive values can play a cognitive role in science, i.e., they can be epistemically beneficial to the assessment of scientific theories. On the basis of a case study (the account of human mating in evolutionary psychology), I have argued that feminist values have positively contributed to theory appraisal in various ways, such as by raising sensitivity to evidence that was neglected because of gender bias. Fourth, some philosophers have argued that non-cognitive values play a legitimate role in cases of inductive risk, namely cases in which scientists may wrongly assess scientific hypotheses (e.g. accepting a hypothesis that should be rejected) because of some uncertainty due to mixed results or disagreement on the reliability of methodologies. Mistakes can have consequences that can be morally or economically undesirable and non-cognitive values, some philosophers argue, provide the standards to evaluate and compare these possible consequences. However, little is known on how - specifically - non-cognitive values influence this evaluation. To address this issue, I have conducted an experimental study clarifying how personal features, political values, and specific aspects of a risk (e.g. the chance of incurring Type I vs. Type II errors) determine people’s reasoning about cases of inductive risk. On the basis of these results, I have discussed recent institutional calls for the need to align research agendas and technological development to citizens’ values, needs, and expectations. (shrink)

This article argues that the controversy over genetically modified crops is best understood not in terms of the supposed bias, dishonesty, irrationality, or ignorance on the part of proponents or critics, but rather in terms of differences in values. To do this, the article draws on and extends recent work of the role of values and interests in science, focusing particularly on inductive risk and epistemic risk, and it shows how the GMO debate can help to further (...) our understanding of the various epistemic risks that are present in science and how these risks might be managed. (shrink)

In the first half of the 20 th century, philosophers of science used to sustain that the correct theory acceptance in science derived from their conforming to certain rules. However, from the historicist and practical turn in the philosophy of science, the theory acceptance started to be analyzed based on values rather than on a priori established rules. In this article, I will present four paradigmatic positions on the role of values in science. The first position, articulated by (...) Hugh Lacey, defends a regulative ideal of 'impartiality' in the acceptance of scientific theories as established. The other positions convey arguments that seem to conflict with this ideal. I characterize them as 'blind spot argument', 'inductive risk argument' and 'trust-based acceptance argument'. Based on this analysis, I will defend two main theses. My first thesis is that the ideal of impartiality is compatible with the arguments just mentioned. My second thesis is that the defense of impartiality has the advantage of providing a support for the epistemic authority of properly established scientific knowledge. Following Lacey, I will argue that theories can only be accepted as established under specified domains of phenomena. However, theories are accepted for other purposes, for example, when they are adopted to develop research or when they are endorsed to inform the legitimacy of an action. In such cases of theory acceptance, impartiality is neither possible nor desirable, which requires the support of other ideals. (shrink)

Animal welfare scientists face an acute version of the problem of inductive risk, since they must choose whether to affirm attributions of mental states to animals in advisory contexts, knowing their decisions hold consequences for animal welfare. In such contexts, the burden of proof should be sensitive to the consequences of error, but a framework for setting appropriate burdens of proof is lacking. Through reflection on two cases—pain and cognitive enrichment—I arrive at a tentative framework based on the principle (...) of expected welfare maximization. I then discuss the limitations of this framework and the questions it leaves open. (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)

Current debates over inductive risk and the role 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 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 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, which is traditionally related to the ideal of value free science, should be reconsidered and even prioritized among the responsibilities of scientists. (shrink)

There is a growing consensus among philosophers of science that core parts of the scientific process involve non-epistemic values. This undermines the traditional foundation for public trust in science. In this article I consider two proposals for justifying public trust in value-laden science. According to the first, scientists can promote trust by being transparent about their value choices. On the second, trust requires that the values of a scientist align with the values of an individual member of (...) the public. I argue that neither of these proposals work and suggest an alternative that does better. When scientists must appeal to values in the course of their research, they should appeal to democratic values: the values of the public or its representatives. (shrink)