Bias as an epistemic notion (2022)

Table of Contents
Studies in History and Philosophy of Science Abstract Introduction Section snippets Bias in statistics and beyond Process versus product level Consequences of adopting an epistemic notion of bias Conclusion Acknowledgements References (48) Studies in History and Philosophy of Science Part A Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences Journal of Chronic Diseases Studies in History and Philosophy of Science Part A Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences Studies In History and Philosophy of Science Part A Studies in History and Philosophy of Science Part A Quine as feminist: The radical import of naturalized epistemology Considering bias and conflicts of interest among the included studies Philosophy of medicine The commercialization of the biomedical sciences: (mis)understanding bias History & Philosophy of the Life Sciences Inductive risk and values in science Philosophy of Science Fact and value Atapestry of values: An introduction to values in science How values in scientific discovery and pursuit alter theory appraisal Philosophy of Science Epistemic injustice: Power and the ethics of knowing Does cardiac transplantation prolong life? A reassessment Annals of Internal Medicine The science question in feminism Is Longino's conception of objectivity feminist? Hypatia Why most sugar pills are not placebos Philosophy of Science In defense of meta-analysis Synthese Feminist values, commercial values, and the bias paradox in biomedical research Cited by (1) Agent-based models of scientific interaction Recommended articles (6) FAQs Videos
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Studies in History and Philosophy of Science

Volume 91,

February 2022

, Pages 307-315

Abstract

Once one abandons the ideal of value-free, impartial science, the question of how to distinguish biased from legitimately value-laden science arises. To approach this “new demarcation problem”, I argue that one should distinguish different uses of “bias” in a first step: a narrow sense of bias as systematic deviation from the truth, and a wider sense that covers any kind of tendency impacting scientific reasoning. Secondly, the narrow sense exemplifies an ontological notion of bias, which understands bias in terms of a deviation from an impartial ideal outcome. I propose to replace it with an epistemic notion of bias, which understands biased research as research that we have good reasons to suspect could have been (done) systematically better. From a socio-epistemic perspective, such good reasons to expect better can be found in a lack of responsiveness to conventional standards and/or critical discourse in the scientific community. In short, bias in an epistemic sense consists in a deviation, not from truth but from current best practice. While this turns bias into something that is dependent on time and context, it allows for value-laden research to be unbiased, if there are no good reasons to expect this research to be better.

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Introduction

The new demarcation problem essentially consists in the question of how to distinguish science that is biased or illegitimately value-laden from science that is value-laden in a legitimate way (cf. Holman and Wilholt, forthc). It arises once one abandons old ideals of science as value-free, neutral, and impartial – ideals that have been thoroughly rebutted over the last decades.1 If science is inadequately characterized as an impartial quest for knowledge, though, what can substantiate claims of epistemic integrity or critiques of epistemic deficiency?

This problem has a long tradition in feminist philosophy of science. How could one justify a rejection of sexist science, while at the same time allowing for science shaped by feminist values? Are these not both cases of value-laden science and thus equally problematic? Following Antony (1993), this has been discussed as the “bias paradox”. Antony describes value-laden science as biased science, because she takes partiality to be the defining characteristic of both. According to her, there is an inherent tension in aiming to overcome science impacted by androcentric assumptions and interests, while at the same time rejecting the ideal of pure, impartial objectivity.2

“Once we've acknowledged the necessity and legitimacy of partiality, how do we tell good bias from bad bias?” (Antony, 1993, 115; italics in original)

Feminist philosophers have usually sought to solve this problem by rethinking our accounts of what good, objective science is. For instance, standpoint epistemologists argue that some partial perspectives – namely, those from marginalized social positions – come with a potential for privileged insight into certain social experiences and power relations (e.g., Harding, 1986). Contextual empiricists (in the tradition of Longino, 1990) agree with this insofar as they call for a diversity of viewpoints and the inclusion of researchers from marginalized social locations in order to make implicit (value-laden) assumptions visible and debatable. The idea here is that a diversity of values can, via a transformative process of criticism, screen out idiosyncratic subjective preferences. By contrast, others argue that a diversity of values is insufficient and that we have to rely on the right (e.g., egalitarian, feminist) values to inform our research, assuming that values are ineliminable (e.g., Intemann & de Melo-Martin, 2016; Kourany, 2003).

Rather than engaging with this debate directly, I will approach the problem from a more conceptual angle in this paper, and argue that one root source of the new demarcation problem (or bias paradox) is an imprecise use of the notion of bias, which makes it hard to see the difference between value-laden science and biased science. I am not claiming that this is the only route to dissolve this problem, only that it is one way of illuminating it – a way that operates at a more general level than current standpoint theoretical or contextual empiricist accounts (and can, in principle, be combined with either of those). My starting point is that bias is an oscillating concept that is used to refer to different phenomena in (philosophy of) science and medicine – often without explicitly defining it or specifying a particular use in a particular context. I will argue that it is, firstly, helpful to differentiate between a narrow usage of “bias” in statistics (often described as “a systematic deviation from the truth” [e.g., Boultron etal., 2019]) and a wider usage of the term covering any kind of tendency in our reasoning, which may stem, e.g., from methodological flaws, common cognitive heuristics, values, prejudice, or interests.

This differentiation is important in order to avoid an overgeneralization of the defining characteristics of one specific use of a term, transforming it to the term's general meaning. In the case at hand, the understanding of “bias” as a deviation from the truth, which describes a statistical distortion in a specific context, should not be taken to apply to all usages of the term – especially because this begs the question of whether any sort of tendency or partiality is epistemically problematic. If all these tendencies constitute or lead to bias, bias is a systematic deviation from the truth, and science aims for the truth, then such tendencies (including those introduced by value judgments) can be nothing but epistemically detrimental.

Even granting this distinction between bias in a wide versus narrow sense, it still seems useful though to have some sort of overarching understanding of “bias”, since the term is often used in (philosophy of) science to express in a very general manner that there is some problematic lack in scientific quality. The desideratum, accordingly, is to have a concept of bias that can point to such problems without rendering value-influences problematic per se. For this purpose, I will distinguish between an ontological and epistemic understanding of bias.

The narrow sense of bias as a systematic deviation from the truth (popular in the health sciences and beyond) is an example of an ontological notion of bias. Generally, an ontological interpretation of bias assumes an ideal and, importantly, impartial outcome (such as the truth or a correct representation of the world) and defines “bias” in terms of deviation from this outcome. The world is in a certain state and our goal is to match this reality with our results (hence, “ontological”). Such an ontological understanding of bias is ill-suited as an overarching understanding of bias. It leads to the bias paradox of how to distinguish between legitimate and illegitimate tendencies and values in scientific reasoning, as it is not clear that one set of values or another is capable of matching reality better.3 Moreover, the debate on science and values has demonstrated the variety and subtlety of potential value-influences in science, not all of which can be usefully conceptualized in terms of conducing to or diverging from the truth or are apt to statistical assessment.

Therefore, I propose to replace this ontological notion with an epistemic one. The epistemic notion of bias understands biased science not as science deviating from some ideal outcome, but as science that we have good reasons to suspect could have been (done) systematically better. This allows us to characterize science as biased even if we give up the ideals of impartiality and value-freedom – provided that we have good reasons to expect better. At the same time, it also allows for the possibility of value-laden, yet legitimate research in cases were there are no such reasons (Section 2).

Section 3 will discuss what such reasons to expect better can look like. In general, I will focus on the procedural dimension of how scientific results are generated. I will distinguish between two different levels on which we can find reasons to expect better: the process of research itself, and the process of establishing standards governing this research. Firstly, drawing on Wilholt (2009), we can suspect that research could have been done better if it deviates from current conventional standards. Secondly, these standards might themselves be problematic, which makes it necessary to look at meta-level norms for the discussion of such conventional standards. Here, Longino's model of social objectivity (1990) provides a potential source for such meta-level norms.

Section 4 goes over some of the implications of my proposal. In contrast to an ontological reading, an epistemic interpretation of the notion of bias renders the correct application of “bias” time- and context-dependent, which can be seen as a disadvantage. At the same time, the epistemic notion is the more helpful overarching understanding of bias: Firstly, it allows for good value-laden research; that is, research that is value-laden yet in compliance with current best practice. It thus dissolves the bias paradox. Secondly, it is applicable to a wider range of potential value-impacts in science. Thirdly, it is compatible with the increased focus on objectivity as procedural rather than as a view from nowhere. Last but not least, it provides a template for identifying biased research. I will finish with a brief conclusion summarizing the main points.

Section snippets

Bias in statistics and beyond

“Bias” is commonly used to refer to unwanted impacts on science in a rather imprecise (or at least very broad) manner. Put generally, the concept is used to describe a variety of mechanisms that bring some sort of tendency into (scientific) reasoning. These mechanisms include (but may exceed) the following.

Firstly, cognitive bias refers to common, usually unconscious tendencies in individual reasoning that can be problematic, such as confirmation bias or anchoring bias. While cognitive biases

Process versus product level

The epistemic notion of bias defines biased science as science that we have good reasons to believe could have been (done) systematically better. The decisive question now becomes what such good reasons to expect better may look like. To approach this question, it is, first of all, important to notice an additional layer of complexity in our use of the concept of bias: it can refer both to the process and results of research (as well as to inferences drawn from these results). This is clearly

Consequences of adopting an epistemic notion of bias

I have proposed to understand bias as an epistemic notion. According to this, research is biased if we have good reasons to believe that it could have been (done) systematically better. Such good reasons can either apply to deviations from existing conventional standards, or they can target these standards themselves as methodologically inadequate or as resulting from an insufficiently transformative critical process.

Often, these discussions will be controversial – there might be a lack of

Conclusion

The ideal of value-free, impartial science has been thoroughly discredited over the last decades. Yet, there are clearly cases of value-laden or interest-driven science that are epistemically deficient. How can these be distinguished from cases of value-laden science that are epistemically legitimate? Don't all value-impacts equally entail partiality and thus result in biased science? In response to this “new demarcation problem”, I have argued that we need to distinguish a narrow statistical

Acknowledgements

I want to thank Jacob Busch and Bennett Holman for helpful comments on earlier versions of this paper, as well as two anonymous reviewers for their careful reading and constructive criticisms.

References (48)

  • T. WilholtBias and values in scientific research

    Studies in History and Philosophy of Science Part A

    (2009)

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  • J. StegengaIs meta-analysis the platinum standard of evidence?

    Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences

    (2011)

  • D.L. SackettBias in analytic research

    Journal of Chronic Diseases

    (1979)

  • A. LeuschnerPluralism and objectivity: Exposing and breaking a circle

    Studies in History and Philosophy of Science Part A

    (2012)

  • S. JukolaOn the evidentiary standards for nutrition advice

    Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences

    (2019)

  • M. Fernández PintoPhilosophy of science for globalized privatization: Uncovering some limitations of critical contextual empiricism

    Studies In History and Philosophy of Science Part A

    (2014)

  • A. BueterThe irreducibility of value-freedom to theory assessment

    Studies in History and Philosophy of Science Part A

    (2015)

  • L. Antony

    Quine as feminist: The radical import of naturalized epistemology

  • I. Boutron et al.

    Considering bias and conflicts of interest among the included studies

  • A. Broadbent

    Philosophy of medicine

    (2018)

  • I. De Melo-Martin

    The commercialization of the biomedical sciences: (mis)understanding bias

    History & Philosophy of the Life Sciences

    (2019)

  • H. Douglas

    Inductive risk and values in science

    Philosophy of Science

    (2000)

  • J. Dupré

    Fact and value

  • K.C. Elliott

    Atapestry of values: An introduction to values in science

    (2017)

  • K.C. Elliott et al.

    How values in scientific discovery and pursuit alter theory appraisal

    Philosophy of Science

    (2009)

  • M. Fricker

    Epistemic injustice: Power and the ethics of knowing

    (2007)

  • M.H. Gail

    Does cardiac transplantation prolong life? A reassessment

    Annals of Internal Medicine

    (1972)

  • S.G. Harding

    The science question in feminism

    (1986)

  • D. Hicks

    Is Longino's conception of objectivity feminist?

    Hypatia

    (2011)

  • B. Holman

    Why most sugar pills are not placebos

    (Video) Session 10, Jesper Kallestrup - From Epistemic Anti-Individualism to Epistemic Humility

    Philosophy of Science

    (2015)

  • B. Holman

    In defense of meta-analysis

    Synthese

    (2019)

  • Holman, B., & Wilholt, T. (forthc). The new demarcation problem. Studies in History and Philosophy of Science Part...
  • K. Intemann et al.

    Feminist values, commercial values, and the bias paradox in biomedical research

  • Cited by (1)

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    FAQs

    What is an epistemic bias? ›

    Epistemic injustice occurs when a person is wronged in their capacity as a knower. It turns out, there are various ways in which one can be wronged as a knower.

    What are the 3 types of bias? ›

    Three types of bias can be distinguished: information bias, selection bias, and confounding. These three types of bias and their potential solutions are discussed using various examples.

    What is the main idea of bias? ›

    Bias is a disproportionate weight in favor of or against an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief. In science and engineering, a bias is a systematic error.

    What is epistemic responsibility in philosophy? ›

    Epistemic responsibility develops through the cultivation of some basic epistemic virtues, such as open-mindedness, epistemic humility, and diligence that help knowers engage in seeking information about themselves, others, and the world that they inhabit (Medina 2013).

    What is meant by epistemology? ›

    epistemology, the philosophical study of the nature, origin, and limits of human knowledge. The term is derived from the Greek epistēmē (“knowledge”) and logos (“reason”), and accordingly the field is sometimes referred to as the theory of knowledge.

    What is bias examples? ›

    Biases are beliefs that are not founded by known facts about someone or about a particular group of individuals. For example, one common bias is that women are weak (despite many being very strong). Another is that blacks are dishonest (when most aren't).

    What does it mean to show bias answer? ›

    Bias, prejudice mean a strong inclination of the mind or a preconceived opinion about something or someone. A bias may be favorable or unfavorable: bias in favor of or against an idea.

    What causes bias? ›

    Bias is acquired at a young age, often as a result of one's upbringing. This unconscious bias becomes problematic when it causes an individual or a group to treat others poorly as a result of their gender, ethnicity, race, or other factors.

    What are the 4 biases? ›

    Here are four of the primary biases that can have an impact on how you lead your team and the decisions you make.
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    What are the most common biases? ›

    Some examples of common biases are:
    • Availability bias. ...
    • Fundamental attribution error. ...
    • Hindsight bias. ...
    • Anchoring bias. ...
    • Optimism bias. ...
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    • The halo effect. ...
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    Bias is an inclination or prejudice for or against one person or group. So, unconscious biases are unconscious feelings we have towards other people – instinctive feelings that play a strong part in influencing our judgements away from being balanced or even-handed.

    What is bias in simple words? ›

    Definition of bias

    (Entry 1 of 4) 1a : an inclination of temperament or outlook especially : a personal and sometimes unreasoned judgment : prejudice. b : an instance of such prejudice. c : bent, tendency.

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    “In reality, bias can be bad or wrong … but it can also be tremendously helpful.” One example of an instance in which bias is helpful, said Ross, is when someone is approached by a stranger with a knife in their hand. That stranger then raises the knife over their head.

    Why is epistemic responsibility important? ›

    Individuals therefore have significant epistemic responsibilities when it comes to their health and wellbeing and (perhaps even more) the health and wellbeing of those who are dependent on them. We expect parents to vet the people in whose care they leave their children, for example.

    Which of the following are the two conditions of the epistemic condition for moral responsibility? ›

    The first condition has to do with whether the agent possessed an adequate degree of control or freedom in performing the action, whereas the second condition is concerned with whether the agent's epistemic or cognitive state was such that she can properly be held accountable for the action and its consequences.

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    What is the example of epistemic? ›

    Examples of Epistemology

    There are three main examples or conditions of epistemology: truth, belief and justification. First of all, truth occurs when false propositions cannot be discerned. Consequently, in order for something to be considered as knowledge, it must be true in nature.

    What is epistemological reasoning? ›

    Reasoning about knowledge, especially reasoning that involves modal logic.

    What is epistemology in research example? ›

    In simple terms, epistemology is the theory of knowledge and deals with how knowledge is gathered and from which sources. In research terms your view of the world and of knowledge strongly influences your interpretation of data and therefore your philosophical standpoint should be made clear from the beginning.

    What are the most common biases? ›

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    What is bias and its types? ›

    Bias is an irrational assumption or belief that affects the ability to make a decision based on facts and evidence. Investors are as vulnerable as anyone to making decisions clouded by prejudices or biases. Smart investors avoid two big types of bias—emotional bias and cognitive bias.

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    When it comes to human behavior, there are many common types of bias we have that can influence the way we think and act in our everyday lives. Confirmation bias.

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