Fricker shows that virtue epistemology provides a general epistemological idiom in which these issues can be forcefully discussed.

In their article, Sedlakova and Trachsel (2023) propose a holistic, ethical, and epistemic analysis of conversational artificial intelligence (CAI) in psychotherapeutic settings. They mainly descri...

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)

Philosophy &Public Affairs, Volume 51, Issue 2, Page 166-190, Spring 2023.

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) health, and judicial (...) contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed. (shrink)

This outstanding reference source to epistemic injustice is the first collection of its kind. Over thirty chapters address topics such as testimonial and hermeneutic injustice and virtue epistemology, objectivity and objectification, implicit bias, gender and race.

We survey several ways in which the structures and norms of medicine and healthcare can generate epistemic injustice.

Many critics of medicalization express concern that the process privileges individualised, biologically grounded interpretations of medicalized phenomena, inhibiting understanding and communication of aspects of those phenomena that are less relevant to their biomedical modelling. I suggest that this line of critique views medicalization as a hermeneutical injustice—a form of epistemic injustice that prevents people having the hermeneutical resources available to interpret and communicate significant areas of their experience. Interpreting the critiques in this fashion shows they frequently fail because they: neglect (...) the ways in which medicalization may not obscure, but rather illuminate, individuals’ experiences; and neglect the testimony of those experiencing first-hand medicalized problems, thus may be guilty of perpetrating testimonial injustice. However, I suggest that such arguments are valuable insofar as they highlight the unwarranted epistemic privilege frequently afforded to medical institutions and medicalized models of phenomena, and a consequent need for greater epistemic humility on the part of health workers and researchers. (shrink)

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)

Over the past decade, feminist philosophers have gone a long way toward identifying and explaining the phenomenon that has come to be known as epistemic injustice. Epistemic injustice is injustice occurring within the domain of knowledge (e.g., knowledge production and transmission), which typically impacts structurally marginalized social groups. In this paper, we argue that, as they currently work, algorithms on social media exacerbate the problem of epistemic injustice and related problems of social distrust. In other words, we argue that algorithms (...) on social media recreate and reify the conditions that lead to some groups being systematically denied the full status of knowers, thereby corrupting the epistemic terrain and, with it, systems of social trust and cooperation. We argue that algorithms do this in two ways—namely, via what we are calling algorithmic targeting and algorithmic sorting. (shrink)

Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby consider how ML systems may silence patients’ voices and relativise (...) the credibility of their opinions, which undermines their overall credibility status without valid moral and epistemic justification. More specifically, I argue that withholding credibilitydue tohow ML systems operate can be particularly harmful to patients and, apart from adverse outcomes, qualifies as a form of testimonial injustice. I make my case for testimonial injustice in medical ML by considering ML systems currently used in the USA to predict patients’ risk of misusing opioids (automated Prediction Drug Monitoring Programmes, PDMPs for short). I argue that the locus of testimonial injustice in ML-mediated medical encounters is found in the fact that these systems are treated asmarkers of trustworthinesson which patients’ credibility is assessed. I further show how ML-based PDMPs exacerbate and further propagate social inequalities at the expense of vulnerable social groups. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ likelihood (...) of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

In response to growing concerns of bias, discrimination, and unfairness perpetuated by algorithmic systems, the datasets used to train and evaluate machine learning models have come under increased scrutiny. Many of these examinations have focused on the contents of machine learning datasets, finding glaring underrepresentation of minoritized groups. In contrast, relatively little work has been done to examine the norms, values, and assumptions embedded in these datasets. In this work, we conceptualize machine learning datasets as a type of informational infrastructure, (...) and motivate a genealogy as method in examining the histories and modes of constitution at play in their creation. We present a critical history of ImageNet as an exemplar, utilizing critical discourse analysis of major texts around ImageNet’s creation and impact. We find that assumptions around ImageNet and other large computer vision datasets more generally rely on three themes: the aggregation and accumulation of more data, the computational construction of meaning, and making certain types of data labor invisible. By tracing the discourses that surround this influential benchmark, we contribute to the ongoing development of the standards and norms around data development in machine learning and artificial intelligence research. (shrink)

In this essay I argue that epistemic injustices can be understood and explained as social pathologies of recognition, and that this way of conceptualizing epistemic injustices can help us develop proper diagnostic and corrective treatments for them. I distinguish between two different kinds of recognition deficiency—quantitative recognition deficits and misrecognitions—and I ague that while the rectification of the former simply requires more recognition, the rectification of the latter calls for a shift in the mode of recognition, that is, a deep (...) transformation of the recognition dynamics so that other forms of recognition can emerge. Arguing against incremental recognitional approaches that aim only at increasing social visibility/audibility, I examine communicative dysfunctions around the phenomenon of racist violence in order to show how problems of misrecognition persist and become recalcitrant even when quantitative recognition deficits disappear. (shrink)

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in healthcare as a case study, we argue that AI bias is a form of (...) structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is about pursuing a more just social structure with the development and usage of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action. (shrink)

According to Russell and Norvig’s (2009) classification, Artificial Intelligence (AI) is the field that aims at building systems which either think rationally, act rationally, think like humans, or...

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data (...) processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM. (shrink)

The “privacy paradox” refers to the discrepancy between the concern individuals express for their privacy and the apparently low value they actually assign to it when they readily trade personal information for low-value goods online. In this paper, I argue that the privacy paradox masks a more important paradox: the self-management model of privacy embedded in notice-and-consent pages on websites and other, analogous practices can be readily shown to underprotect privacy, even in the economic terms favored by its advocates. The (...) real question, then, is why privacy self-management occupies such a prominent position in privacy law and regulation. Borrowing from Foucault’s late writings, I argue that this failure to protect privacy is also a success in ethical subject formation, as it actively pushes privacy norms and practices in a neoliberal direction. In other words, privacy self-management isn’t about protecting people’s privacy; it’s about inculcating the idea that privacy is an individual, commodified good that can be traded for other market goods. Along the way, the self-management regime forces privacy into the market, obstructs the functioning of other, more social, understandings of privacy, and occludes the various ways that individuals attempt to resist adopting the market-based view of themselves and their privacy. Throughout, I use the analytics practices of Facebook and social networking sites as a sustained case study of the point. (shrink)

Following Foucault’s remarks on the importance of architecture to disciplinary power, this paper offers a typology of power relations expressed in different models of Internet governance. Infrastructure governance understands the Internet as a common pool or public resource, on the model of traditional infrastructures like roads and bridges. Modulation governance, which I study by way of Net Neutrality debates in the U.S., understands Internet governance as traffic shaping. Portal governance, which I study by way of data collection policies of dominant (...) platform companies, understands the Internet as creating a user experience that facilitates data mining. The latter two are forms of architectural disciplinary power that undermine the first. I then argue that the rise of portal and modulation governance primarily serves to remake parts of civil society by fostering market norms of consumption and entrepreneurialism. In that sense, efforts to shape Internet architecture need to be understood as techniques of subjectification. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false (...) positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (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)

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three (...) key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns. (shrink)

Philosophy & Public Affairs, Volume 50, Issue 2, Page 239-266, Spring 2022.

In the era of information and communication, issues of misinformation and miscommunication are more pressing than ever. _Epistemic injustice - _one of the most important and ground-breaking subjects to have emerged in philosophy in recent years - refers to those forms of unfair treatment that relate to issues of knowledge, understanding, and participation in communicative practices. The Routledge Handbook of Epistemic Injustice is an outstanding reference source to the key topics, problems and debates in this exciting subject. The first collection (...) of its kind, it comprises over thirty chapters by a team of international contributors, divided into five parts: Core Concepts Liberatory Epistemologies and Axes of Oppression Schools of Thought and Subfields within Epistemology Socio-political, Ethical, and Psychological Dimensions of Knowing Case Studies of Epistemic Injustice. As well as fundamental topics such as testimonial and hermeneutic injustice and epistemic trust, the Handbook includes chapters on important issues such as social and virtue epistemology, objectivity and objectification, implicit bias, and gender and race. Also included are chapters on areas in applied ethics and philosophy, such as law, education, and healthcare. The Routledge Handbook of Epistemic Injustice is essential reading for students and researchers in ethics, epistemology, political philosophy, feminist theory, and philosophy of race. It will also be very useful for those in related fields, such as cultural studies, sociology, education and law. (shrink)

This paper situates the data privacy debate in the context of what I call the death of the data subject. My central claim is that concept of a rights-bearing data subject is being pulled in two contradictory directions at once, and that simultaneous attention to these is necessary to understand and resist the extractive practices of the data industry. Specifically, it is necessary to treat the problems facing the data subject structurally, rather than by narrowly attempting to vindicate its rights. (...) On the one hand, the data industry argues that subjects of biometric identification lack legal standing to pursue claims in court, and Facebook recently denied that that its facial recognition software recognizes faces. On the other hand, industry takes consent to terms of service and arbitration clauses to create enforceable legal subject positions, while using promises of personalization to create a phenomenological subject that is unaware of the extent to which it is being manipulated. Data subjects thus have no legal existence when it is a matter of corporate liability, but legal accountability when it is a matter of their own liability. Successful reform should address the power asymmetries between individuals and data companies that enable this structural disempowerment. (shrink)

One of the main lines of research in algorithmic fairness involves individual fairness (IF) methods. Individual fairness is motivated by an intuitive principle, similar treatment, which requires that similar individuals be treated similarly. IF offers a precise account of this principle using distance metrics to evaluate the similarity of individuals. Proponents of individual fairness have argued that it gives the correct definition of algorithmic fairness, and that it should therefore be preferred to other methods for determining fairness. I argue that (...) individual fairness cannot serve as a definition of fairness. Moreover, IF methods should not be given priority over other fairness methods, nor used in isolation from them. To support these conclusions, I describe four in-principle problems for individual fairness as a definition and as a method for ensuring fairness: (1) counterexamples show that similar treatment (and therefore IF) are insufficient to guarantee fairness; (2) IF methods for learning similarity metrics are at risk of encoding human implicit bias; (3) IF requires prior moral judgments, limiting its usefulness as a guide for fairness and undermining its claim to define fairness; and (4) the incommensurability of relevant moral values makes similarity metrics impossible for many tasks. In light of these limitations, I suggest that individual fairness cannot be a definition of fairness, and instead should be seen as one tool among several for ameliorating algorithmic bias. (shrink)