Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or correct them. Epistemically unjust harms will typically intersect with other more familiar and well-studied kinds of harm that result from the design, development, and use of data science technologies. However, (...) we argue that epistemic injustices can be distinguished conceptually from more familiar kinds of harm. We argue that epistemic harms are morally relevant even in cases where those who suffer them are unharmed in other ways. Via a series of examples from the criminal justice system, workplace hierarchies, and educational contexts we explain the kinds of epistemic injustice that can result from common uses of data science technologies. (shrink)

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...) diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare. (shrink)

This article analyses the phenomenon of epistemic injustice within contemporary healthcare. We begin by detailing the persistent complaints patients make about their testimonial frustration and hermeneutical marginalization, and the negative impact this has on their care. We offer an epistemic analysis of this problem using Miranda Fricker's account of epistemic injustice. We detail two types of epistemic injustice, testimonial and hermeneutical, and identify the negative stereotypes and structural features of modern healthcare practices that generate them. We claim that these stereotypes (...) and structural features render ill persons especially vulnerable to these two types of epistemic injustice. We end by proposing five avenues for further work on epistemic injustice in healthcare. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

In this paper we argue that ill persons are particularly vulnerable to epistemic injustice in the sense articulated by Fricker. Ill persons are vulnerable to testimonial injustice through the presumptive attribution of characteristics like cognitive unreliability and emotional instability that downgrade the credibility of their testimonies. Ill persons are also vulnerable to hermeneutical injustice because many aspects of the experience of illness are difficult to understand and communicate and this often owes to gaps in collective hermeneutical resources. We then argue (...) that epistemic injustice arises in part owing to the epistemic privilege enjoyed by the practitioners and institutions of contemporary healthcare services—the former owing to their training, expertise, and third-person psychology, and the latter owing to their implicit privileging of certain styles of articulating and evidencing testimonies in ways that marginalise ill persons. We suggest that a phenomenological toolkit may be part of an effort to ameliorate epistemic injustice. (shrink)

In Epistemic injustice, Miranda Fricker makes a tremendous contribution to theorizing the intersection of social epistemology with theories of justice. Theories of justice often take as their object of assessment either interpersonal transactions (specific exchanges between persons) or particular institutions. They may also take a more comprehensive perspective in assessing systems of institutions. This systemic perspective may enable control of the cumulative effects of millions of individual transactions that cannot be controlled at the individual or institutional levels. This is true (...) not only with respect to the overall distribution of such goods as income and wealth, but also with respect to the goods of testimonial and hermeneutical justice. Cognitive biases that may be difficult for even epistemically virtuous individuals to correct on their own may be more susceptible to correction if we focus on the principles that should govern our systems of testimonial gathering and assessment. Hence, while Fricker’s focus on individual epistemic virtue is important, we also need to consider what epistemic justice as a virtue of social systems would require. My paper will indicate some directions forward on this front, focusing on the need for integration of diverse institutions and persons engaged in inquiry. (shrink)

Trust is central to the therapeutic relationship, but the epistemic asymmetries between the expert healthcare provider and the patient make the patient, the trustor, vulnerable to the provider, the trustee. The narratives of pain sufferers provide helpful insights into the experience of pain at the juncture of trust, expert knowledge, and the therapeutic relationship. While stories of pain sufferers having their testimonies dismissed are well documented, pain sufferers continue to experience their testimonies as being epistemically downgraded. This kind of epistemic (...) injustice has received limited treatment in bioethics. In this paper, we examine how a climate of distrust in pain management may facilitate what Fricker calls epistemic injustice. We critically interrogate the processes through which pain sufferers are vulnerable to specific kinds of epistemic injustice, such as testimonial injustice. We also examine how healthcare institutions and practices privilege some kinds of evidence and ways of knowing while excluding certain patient testimonies from epistemic consideration. We argue that providers ought to avoid epistemic injustice in pain management by striving toward epistemic humility. Epistemic humility, as a form of epistemic justice, may be the kind disposition required to correct the harmful prejudices that may arise through testimonial exchange in chronic pain management. (shrink)

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...) computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

In this paper I argue that it is morally important for doctors to trust patients. Doctors' trust of patients lays the foundation for medical relationships which support the exercise of patient autonomy, and which lead to an enriched understanding of patients' interests. Despite the moral and practical desirability of trust, distrust may occur for reasons relating to the nature of medicine, and the social and cultural context within which medical care is provided. Whilst it may not be possible to trust (...) at will, the conscious adoption of a trusting stance is both possible and warranted as the burdens of misplaced trust fall more heavily upon patients than doctors. (shrink)

Artificial intelligence (AI) is increasingly being developed for use in medicine, including for diagnosis and in treatment decision making. The use of AI in medical treatment raises many ethical issues that are yet to be explored in depth by bioethicists. In this paper, I focus specifically on the relationship between the ethical ideal of shared decision making and AI systems that generate treatment recommendations, using the example of IBM’s Watson for Oncology. I argue that use of this type of system (...) creates both important risks and significant opportunities for promoting shared decision making. If value judgements are fixed and covert in AI systems, then we risk a shift back to more paternalistic medical care. However, if designed and used in an ethically informed way, AI could offer a potentially powerful way of supporting shared decision making. It could be used to incorporate explicit value reflection, promoting patient autonomy. In the context of medical treatment, we need value-flexible AI that can both respond to the values and treatment goals of individual patients and support clinicians to engage in shared decision making. (shrink)