It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, (...) we not only demonstrate how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- (...) ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

If whatever counts as merit for some purpose is unevenly distributed, a decision procedure that accurately sorts people on that basis will “pick up” and reproduce the pre-existing pattern in ways that more random, less merit-tracking procedures would not. This dynamic is an important cause for concern about the use of predictive models to allocate goods and opportunities. In this article, I distinguish two different objections that give voice to that concern in different ways. First, decision procedures may contribute to (...) future social injustice and other social ills by sustaining or aggravating patterns that undermine equality of status and opportunity. Second, the same decision procedures may wrong particular individuals by compounding prior injustices that explain those persons’ predicted or actual characteristics. I argue for the importance of the first idea and raise doubts about the second. In normative assessments and legal regulation of algorithmic decisionmaking, as in our thinking about anti-discrimination norms more broadly, a central concern ought to be the prospect of entrenching harmful and unjust patterns—quite apart from any personal wrong done to the individuals about whom predictions are made. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered (...) by conflations of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be “treated as an individual.” I suggest that the right encodes agents’ entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally to avoid (...) them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking. (shrink)

Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can (...) be handled more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

Many of the benefits and burdens we might experience in our lives — from bank loans to bail terms — are increasingly decided by institutions relying on algorithms. In a sense, this is nothing new: algorithms — instructions whose steps can, in principle, be mechanically executed to solve a decision problem — are at least as old as allocative social institutions themselves. Algorithms, after all, help decision-makers to navigate the complexity and variation of whatever domains they are designed for. In (...) another sense, however, this development is startlingly new: not only are algorithms being deployed in ever more social contexts, they are being mechanically executed not merely in principle, but pervasively in practice. Due to recent advances in computing technology, the benefits and burdens we experience in our lives are now increasingly decided by automata, rather than each other. How are we to morally assess these technologies? In this chapter, I propose a preliminary conceptual schema for identifying and locating the various injustices of automated, algorithmic social decision systems, from a broadly liberal perspective. (shrink)

In this position paper, we introduce a new epistemic lens for analyzing algorithmic harm. We argue that the epistemic lens we propose herein has two key contributions to help reframe and address some of the assumptions underlying inquiries into algorithmic fairness. First, we argue that using the framework of epistemic injustice helps to identify the root causes of harms currently framed as instances of representational harm. We suggest that the epistemic lens offers a theoretical foundation for expanding (...) approaches to algorithmic fairness in order to address a wider range of harms not recognized by existing technical or legal definitions. Second, we argue that the epistemic lens helps to identify the epistemic goals of inquiries into algorithmic fairness. There are two distinct contexts within which we examine algorithmic harm: at times, we seek to understand and describe the world as it is, and, at other times, we seek to build a more just future. The epistemic lens can serve to direct our attention to the epistemic frameworks that shape our interpretations of the world as it is and the ways we envision possible futures. Clarity with respect to which epistemic context is relevant in a given inquiry can further help inform choices among the different ways of measuring and addressing algorithmic harms. We introduce this framework with the goal of initiating new research directions bridging philosophical, legal, and technical approaches to understanding and mitigating algorithmic harms. (shrink)

The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy multiple criteria of fairness. This paper contributes to the literature stemming from the impossibility theorems by examining how informational richness affects the accuracy and fairness of predictive algorithms. With the aid of a computer simulation, (...) we show that informational richness is the engine that drives improvements in the performance of a predictive algorithm, in terms of both accuracy and fairness. The centrality of informational richness suggests that classification parity, a popular criterion of algorithmic fairness, should be given relatively little weight. But we caution that the centrality of informational richness should be taken with a grain of salt in light of practical limitations, in particular, the so-called bias-variance trade off. (shrink)

From music recommendation to assessment of asylum applications, machine-learning algorithms play a fundamental role in our lives. Naturally, the rise of AI implementation strategies has brought to public attention the ethical risks involved. However, the dominant anti-discrimination discourse, too often preoccupied with identifying particular instances of harmful AIs, has yet to bring clearly into focus the more structural roots of AI-based injustice. This paper addresses the problem of AI-based injustice from a distinctively epistemic angle. More precisely, I argue (...) that the injustice generated by the implementation of AI machines in our societies is, in some paradigmatic cases, also a form of epistemic injustice. With a particular focus on AIs employed as gatekeepers of our epistemic resources, this paper shows how their epistemically conformist behaviour is responsible for the marginalisation and the ostracism of minoritarian perspectives. Because it clarifies key structural flaws and weaknesses of current AI design, this paper helps make headway in critical discussion of current AI technologies. And because it forges new theoretical tools to understand forms of epistemic oppression, this paper also contributes to the advancement of feminist theorisation. (shrink)

It is becoming more common that the decision-makers in private and public institutions are predictive algorithmic systems, not humans. This article argues that relying on algorithmic systems is procedurally unjust in contexts involving background conditions of structural injustice. Under such nonideal conditions, algorithmic systems, if left to their own devices, cannot meet a necessary condition of procedural justice, because they fail to provide a sufficiently nuanced model of which cases count as relevantly similar. Resolving this problem (...) requires deliberative capacities uniquely available to human agents. After exploring the limitations of existing formal algorithmic fairness strategies, the article argues that procedural justice requires that human agents relying wholly or in part on algorithmic systems proceed with caution: by avoiding doxastic negligence about algorithmic outputs, by exercising deliberative capacities when making similarity judgments, and by suspending belief and gathering additional information in light of higher-order uncertainty. (shrink)

The aim of this project to expose the reasons behind the pandemic of misinformation (henceforth, PofM) by examining the enabling conditions of epistemic agency and the emerging technologies that threaten it. I plan to research the emotional origin of epistemic agency, i.e. on the origin of our capacity to acquire justification for belief, as well as on the significance this emotional origin has for our lives as epistemic agents in our so-called Misinformation Age. This project has three objectives. First, I (...) plan to expose the degree to which epistemic agency is made possible by an under-researched species of emotion called epistemic feelings. Perhaps, the most epistemically significant is the feeling of confidence. In particular, epistemic feelings make epistemic agency possible by making errors in reasoning salient or the potential lack thereof. -/- Second, in order to diagnose the reasons for PofM, I will analyze the emotional basis of epistemic agency in the context of emerging technologies. Epistemic feelings ought to be construed as motivators of epistemic acts, specifically acts following exposure to misinformation spread by social media. For example, a recent study found that subjects on YouTube have a 6.3% probability in five clicks to go from watching innocuous videos to misogynistic videos. Accordingly, one prominent view holds that social media algorithms lead subjects down the proverbial rabbit hole, producing content that elicits strong emotional reactions. -/- But under-researched is the degree to which these emotions are intertwined with epistemic feelings, so that subjects are likely to misjudge the information as correct due to being primed to do so by processing the AI's suggested media. Thus, when this technology is used in epistemic practices, the result is invariably epistemically bad behavior because of how this manner of producing content motivates epistemic acts through the production of erroneous feelings of confidence, which result from the perceived ease of cognitive processing: the content is easy to process and so, the subject confidently misjudges, it is true. As of yet, an account of this cognitive and emotional decision-making process has not been used to analyze how emerging technologies are threatening epistemic agency, and thus our wellbeing as knowers. -/- Finally, I plan to develop therapeutics for PofM by outlining how to reform our collective epistemic practices. Thus, this project complements existing research in virtue epistemology, specifically by articulating the challenges to cultivating epistemic virtues and warding off the vices of the mind. In summation, the project aims to account for how emerging technologies are committing a form of epistemic injustice through the exploitation of cognitive biases and the production of erroneous epistemic feelings, with the overarching goal being a framework for convalescing from the pandemic of misinformation plaguing our age. (shrink)

Endre Begby’s Prejudice: A Study in Non-Ideal Epistemology engages a wide range of issues of enduring interest to epistemologists, applied ethicists, and anyone concerned with how knowledge and justice intersect. Topics include stereotypes and generics, evidence and epistemic justification, epistemic injustice, ethical-epistemic dilemmas, moral encroachment, and the relations between blame and accountability. Begby applies his views about these topics to an equally wide range of pressing social questions, such as conspiracy theories, misinformation, algorithmic bias, discrimination, and criminal justice. (...) Through it all, the book’s central thesis is that prejudices can be epistemically rational, a corrective against what Begby takes to be the received view that prejudices are always and everywhere bad. However, Begby’s arguments do not engage consistently with relevant empirical literatures, misrepresent the positions of his interlocutors, and rehearse ideas already well-established across a range of intellectual traditions. (shrink)

Algorithms wield increasing control over our lives—over which jobs we get, whether we're granted loans, what information we're exposed to online, and so on. Algorithms can, and often do, wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has gone largely neglected. I investigate three questions about algorithmic neutrality: What is it? Is it possible? And when we have it in mind, what can we learn about (...) algorithmic bias? (shrink)

The literature on epistemic injustice has thus far confined the concept of testimonial injustice to speech expressions such as inquiring, discussing, deliberating, and, above all, telling. I propose that it is time to broaden the horizons of testimonial injustice to include a wider range of expressions. Controversially, the form of communication I have in mind is non-verbal expression. Non-verbal expression is a vital, though often overlooked, form of communication, particularly for people who have certain neurocognitive disorders. Dependency (...) upon non-verbal expression is a common feature of some forms of neurocognitive disorders such as ‘intellectual disabilities’, autism and late-stage dementia. According to the narrow definition of testimonial injustice currently championed in the literature, people who express non-verbally are exempt from testimonial injustice. However, when we consider cases where meaningful communications from non-verbal people are dismissed or ignored in virtue of identity prejudice, there seems to be a distinct testimonial harm at play. Using late-stage dementia as a case study, I argue that the definition of testimonial injustice should be expanded to include all communicative practices, whether verbal or non-verbal, to encompass the epistemic harms inflicted upon some of the most marginalised in our society. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

Anger gaslighting is behavior that tends to make someone doubt herself about her anger. In this paper, I analyze the case of anger gaslighting, using it as a paradigm case to argue that gaslighting can be an affective injustice (not only an epistemic one). Drawing on Marilyn Frye, I introduce the concept of “uptake” as a tool for identifying anger gaslighting behavior (persistent, pervasive uptake refusal for apt anger). But I also demonstrate the larger significance of uptake in the (...) study of affective injustice: just as the concept of credibility names the epistemic behavior whereby we take someone seriously as an epistemic being, the concept of uptake names the uniquely affective cooperative behavior whereby we take someone seriously as an affective being. I answer Miranda Fricker’s epistemic notion of a prejudicial credibility economy with the affective notion of prejudicial uptake economies: uptake, like credibility, can be produced in a deficit for one social group relative to a surplus for another. Deviating from the parallels with Fricker, for whom the injustice of epistemic injustice is due to prejudice in the motives or character of individuals, as well as from accounts that ground it in aptness or affective goods, I suggest that the injustice of anger gaslighting behavior can be located at the structural scale of power relationships between social groups, in the tradition of Iris Marion Young. Anger gaslighting behavior counts as unjust wherever it (re)produces prejudicial uptake economies. Adapting sociological concepts of feeling rules and the emotion work they demand, I introduce the concepts of “uptake rules” and “uptake work” to further enable analysis of uptake economies as affective social structures, and to suggest a site for resistant or reparative affective agency. (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)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers (...) have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

Why does social injustice exist? What role, if any, do implicit biases play in the perpetuation of social inequalities? Individualistic approaches to these questions explain social injustice as the result of individuals’ preferences, beliefs, and choices. For example, they explain racial injustice as the result of individuals acting on racial stereotypes and prejudices. In contrast, structural approaches explain social injustice in terms of beyond-the-individual features, including laws, institutions, city layouts, and social norms. Often these two approaches (...) are seen as competitors. Framing them as competitors suggests that only one approach can win and that the loser offers worse explanations of injustice. In this essay, we explore each approach and compare them. Using implicit bias as an example, we argue that the relationship between individualistic and structural approaches is more complicated than it may first seem. Moreover, we contend that each approach has its place in analyses of injustice and raise the possibility that they can work together—synergistically—to produce deeper explanations of social injustice. If so, the approaches may be complementary, rather than competing. (shrink)

This paper offers an overview of the philosophical work on epistemic injustices as it relates to psychiatry. After describing the development of epistemic injustice studies, we survey the existing literature on its application to psychiatry. We describe how the concept of epistemic injustice has been taken up into a range of debates in philosophy of psychiatry, including the nature of psychiatric conditions, psychiatric practices and research, and ameliorative projects. The final section of the paper indicates future directions for (...) philosophical research of epistemic injustices and psychiatry, concerning neurocognitive disorders, identity prejudices in psychiatric illness, concepts of epistemic privilege in psychiatry, and the prospects for combining phenomenological psychopathology and epistemic justice. We argue that much remains to be done in the conceptualization of these epistemic injustices and suggest that this future work should be multidisciplinary in character and sensitive to the phenomenology of psychiatric conditions. (shrink)

Recent feminist philosophy of language has highlighted the ways that the speech of women can be unjustly impeded, because of the way their gender affects the uptake their speech receives. In this chapter, I explore how similar processes can undermine the speech of a different sort of speaker: Indigenous communities. This involves focusing on Indigeneity rather than gender as the salient social identity, and looking at the ways that group speech, rather than only individual speech, can be unjustly impeded. To (...) do this, I make use of the notion of ‘discursive injustice’ that has been developed by Quill Kukla, and, with reference to three case studies, show how discursive injustice can effectively derail the speech of Indigenous communities. (shrink)

This paper argues that underlying social biases are able to affect the processes underlying linguistic interpretation. The result is a series of harms systematically inflicted on marginalised speakers. It is also argued that the role of biases and stereotypes in interpretation complicates Miranda Fricker's proposed solution to epistemic injustice.

There has been extensive discussion of testimonial epistemic injustice, the phenomenon whereby a speaker’s testimony is rejected due to prejudice regarding who they are. But people also have their testimony rejected or preempted due to prejudice regarding what they communicate. Here, the injustice is content focused. We describe several cases of content focused injustice, and we theoretically interrogate those cases by building up a general framework through which to understand them as a genuine form of epistemic (...) class='Hi'>injustice that stands in intertwined relationships to other forms of epistemic injustice. (shrink)

Although previous treatments of affective injustice have identified some particular types of affective injustice, the general concept of affective injustice remains unclear. This article proposes a novel articulation of this general concept, according to which affective injustice is defined as a state in which individuals or groups are deprived of “affective goods” which are owed to them. On this basis, I sketch an approach to the philosophical investigation of affective injustice that begins by establishing which (...) affective goods are fundamental, and then considers which subsidiary goods—such as freedoms, resources, opportunities, and forms of recognition—may be necessary for the provision of those fundamental affective goods. Drawing from and developing ideas in the extant literature, I argue that two such fundamental affective goods include subjective well-being and emotional aptness. I then show that by analyzing deprivations of the subsidiary goods that enable a person to pursue and attain subjective well-being and emotional aptness, it is possible to shed new light on the cases of affective injustice that have been described in the extant literature, while also identifying other kinds of cases that have not been theorized to the same extent. (shrink)

In this article we develop a taxonomy of emotional injustice: what occurs when the treatment of emotions is unjust, or emotions are used to treat people unjustly. After providing an overview of previous work on this topic and drawing inspiration from the more developed area of epistemic injustice, we propose working definitions of ‘emotion’, ‘injustice’, and ‘emotional injustice’. We describe seven classes of emotional injustice: Emotion Misinterpretation, Discounting, Extraction, Policing, Exploitation, Inequality, and Weaponizing. We say (...) why it is useful to distinguish these and also to subsume them under a single concept. Our aims are both theoretical and practical: to provide a unified account of emotional injustice, while recognizing the diversity of this phenomenon; to facilitate further research on this topic; to recognize the political importance of emotions; and to outline some of the ways in which emotional injustice can be combated. (shrink)

Reflection on the historical injustice suffered by many formerly colonized groups has left us with a peculiar account of their claims to material objects. One important upshot of that account, relevant to present day justice, is that many people seem to think that members of indigenous groups have special claims to the use of particular external objects by virtue of their attachment to them. In the first part of this paper I argue against that attachment-based claim. In the second (...) part I suggest that, to provide a normatively defensible account of why sometimes agents who are attached to certain external objects might also have special claims over them, the most important consideration is whether the agents making such claims suffer from structural injustice in the present. In the third part I try to explain why structural injustice matters, in what way attachment-based claims relate to it and when they count. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key (...) ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

There is a growing awareness that there are many subtle forms of exclusion and partiality that affect the correct workings of a judicial system. The concept of epistemic injustice, introduced by the philosopher Miranda Fricker, is a useful conceptual tool to understand forms of judicial partiality that often go undetected. In this paper, we present Fricker’s original theory and some of the applications of the concept of epistemic injustice in legal processes. In particular, we want to show that (...) the seed planted by Fricker has flourished into a rich field of study in which the concept is used to analyze many different phenomena in law, not always following the original characterization provided by her. This has led to a distinction between what we will call the narrow version of the concept, which is closer to Fricker’s original account, and the wider version of epistemic injustice, which is a more controversial notion because it is always on the verge of morphing into other well-known concepts like sexism, racial discrimination, oppression, silencing, and gaslighting. We will show that the value of the narrow version is mostly theoretical, and that in order to use the concept of epistemic injustice one must adopt a more liberal understanding of it. (shrink)

Contextualist treatments of clashes of intuitions can allow that two claims, apparently in conflict, can both be true. But making true utterances is far from the only thing that matters — there are often substantive normative questions about what contextual parameters are appropriate to a given conversational situation. This paper foregrounds the importance of the social power to set contextual standards, and how it relates to injustice and oppression, introducing a phenomenon I call "contextual injustice," which has to (...) do with the unjust manipulation of conversational parameters in context-sensitive discourse. My central example applies contextualism about knowledge ascriptions to questions about knowledge regarding sexual assault allegations, but I will also discuss parallel dynamics in other examples of context-sensitive language involving politically significant terms, including gender terms. The central upshot is that the connections between language, epistemology, and social justice are very deeply interlinked. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...) clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

Miranda Fricker’s account of hermeneutical injustice and remedies for this injustice are widely debated. This article adds to the existing debate by arguing that theories of recog- nition can fruitfully contribute to Fricker’s account of hermeneutical injustice and can provide a framework for structural remedy. By pairing Fricker’s theory of hermeneutical injustice with theories of recognition, I bring forward a modest claim and a more radical claim. The first concerns a shift in our vocabulary; recognition theory (...) can give a name to the seriousness of the long-term effects of hermeneutical injustice. The second claim is more radical: thinking of hermeneutical injustice as preventing what I call “self-recogni- tion” provides a structural remedy to the phenomenon of hermeneutical injustice. Because hermeneutical injustice is first and foremost a structural injustice, I contend that every virtue theory of hermeneutical justice should be complemented by structural remedies in terms of recognition. Finally, what I argue sheds light on the seriousness of cases of exclusion of and discrimination against women in academia and helps to draw our attention to new ways to combat such problems. (shrink)

The practice of Emergency Management in Michigan raises anew the question of whose knowledge matters to whom and for what reasons, against the background of what projects, challenges, and systemic imperatives. In this paper, I offer a historical overview of state intervention laws across the United States, focusing specifically on Michigan’s Emergency Manager laws. I draw on recent analyses of these laws to develop an account of a phenomenon that I call epistemic redlining, which, I suggest, is a form of (...) group-based credibility discounting not readily countenanced by existing, ‘culprit-based’ accounts of epistemic injustice. I argue that epistemic redlining plays a crucial role in ongoing projects of racialized subordination and dispossession in Michigan, and that such discounting tends to have structural causes that can be difficult to identify and uproot. Contrary to the general thrust of recent work on the topic, I argue that epistemic redlining ought to be understood as a form of epistemic injustice. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and (...) fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...) consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

It is often argued that our obligations to address structural injustice are collective in character. But what exactly does it mean for ‘ordinary citizens’ to have collective obligations visà- vis large-scale injustice? In this paper, I propose to pay closer attention to the different kinds of collective action needed in addressing some of these structural injustices and the extent to which these are available to large, unorganised groups of people. I argue that large, dispersed and unorganised groups of (...) people are often in a position to perform distributive collective actions. As such, ordinary citizens can have massively shared obligations to address structural injustice through distributive action, but, ultimately, such obligations are ‘collective’ only in a fairly weak sense. (shrink)

To verify the occurrence of a singular instance of testimonial injustice three facts must be established. The first is whether the hearer in fact has an identity prejudice of which she may or may not be aware; the second is whether that prejudice was in fact the cause of the unjustified credibility deficit; and the third is whether there was in fact a credibility deficit in the testimonial exchange. These three elements constitute the facts of the matter of testimonial (...) injustice. In this essay we argue that none of these facts can be established with any degree of confidence, and therefore that testimonial injustice is an undetectable phenomenon in singular instances. Our intention is not to undermine the idea of testimonial injustice, but rather to set limits to what can be justifiably asserted about it. According to our argument, although there are insufficient reasons to identify individual acts of testimonial injustice, it is possible to recognize recurrent patterns of epistemic responses to speakers who belong to specific social groups. General testimonial injustice can thus be characterized as a behavioral tendency of a prejudiced hearer. (shrink)

This chapter defends the view that manipulated behaviour is explained by an injustice. Injustices that explain manipulated behaviour need not involve agential features such as intentionality. Therefore, technology can manipulate us, even if technological artefacts like robots, intelligent software agents, or other ‘mere tools’ lack agential features such as intentionality. The chapter thus sketches a comprehensive account of manipulated behaviour related to but distinct from existing accounts of manipulative behaviour. It then builds on that account to defend the possibility (...) that we are being manipulated by technology. (shrink)

Genocide denialism is an understudied topic in the epistemic injustice scholarship; so are epistemic relations outside of the Euro-American context. This article proposes to bring the literature into contact with an underexplored topic in a ‘distant’ setting: Turkey. Here, I explore the ethical and epistemological implications of the Turkish denial of the Armenian genocide as a pervasive and systematic epistemic harm. Using an interdisciplinary methodology, I argue that a philosophical exploration of genocide denialism requires examining the role of institutions (...) and ideology in relation to the epistemic harm done by individual perpetrators. More specifically, I suggest that the individual, ideological, and institutional roots of genocide denialism constitute a regime of epistemic injustice in Turkey. (shrink)

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...) however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those ‘involved’ and ‘uninvolved’ in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people’s claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation. (shrink)

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that (...) people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

We argue that machine learning algorithms can inflict microaggressions on members of marginalized groups and that recognizing these harms as instances of microaggressions is key to effectively addressing the problem. The concept of microaggression is also illuminated by being studied in algorithmic contexts. We contribute to the microaggression literature by expanding the category of environmental microaggressions and highlighting the unique issues of moral responsibility that arise when we focus on this category. We theorize two kinds of algorithmic microaggression, (...) stereotyping and erasure microaggressions, and argue that corporations are responsible for the microaggressions their algorithms create. As a case study, we look at the problems faced by Google’s autocomplete prediction and at the insufficiency of their solutions. The case study of autocomplete demonstrates our core claim that microaggressions constitute a distinct form of algorithmic bias and that identifying them as such allows us to avoid seeming solutions that recreate the same kinds of harms. Google has a responsibility to make information freely available, without exposing users to degradation. To fulfill its duties to marginalized groups, Google must abandon the fiction of neutral prediction and instead embrace the liberatory power of suggestion. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...) science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)

Previously proposed strategies for tackling hermeneutical injustices take for granted the interests people have in certain things about them being intelligible to them and/or to others, and seek to enable them to satisfy these interests. Strategies of this sort I call interests-as-given strategies. I propose that some hermeneutical injustices can instead be tackled by doing away with certain of these interests, and so with the possibility of their unfair non-satisfaction. Strategies of this sort I call interests-in-question strategies. As a case (...) study in when such an interests-in-question strategy ought to be pursued, I look at how to tackle hermeneutical injustices arising in the context of gender-affirming healthcare as provided to adults by the National Health Service in the UK. I argue that considerations of trust, privacy, and respect all support pursuing such a strategy. One way to do so, I suggest, would be by replacing the existing gatekeeping model with an informed consent model for the provision of gender-affirming healthcare. Considerations of hermeneutical justice can hence be added to the already-impressive case for undertaking this shift. (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)

Chronic fatigue syndrome or myalgic encephalomyelitis remains a controversial illness category. This paper surveys the state of knowledge and attitudes about this illness and proposes that epistemic concerns about the testimonial credibility of patients can be articulated using Miranda Fricker’s concept of epistemic injustice. While there is consensus within mainstream medical guidelines that there is no known cause of CFS/ME, there is continued debate about how best to conceive of CFS/ME, including disagreement about how to interpret clinical studies of (...) treatments. Against this background, robust qualitative and quantitative research from a range of countries has found that many doctors display uncertainty about whether CFS/ME is real, which may result in delays in diagnosis and treatment for patients. Strikingly, qualitative research evinces that patients with CFS/ME often experience suspicion by health professionals, and many patients vocally oppose the effectiveness, and the conceptualization, of their illness as psychologically treatable. We address the intersection of these issues and healthcare ethics, and claim that this state of affairs can be explained as a case of epistemic injustice. We find evidence that healthcare consultations are fora where patients with CFS/ME may be particularly vulnerable to epistemic injustice. We argue that the marginalization of many patients is a professional failure that may lead to further ethical and practical consequences both for progressive research into CFS/ME, and for ethical care and delivery of current treatments among individuals suffering from this debilitating illness. (shrink)

It is frequently argued that to address structural injustice, individuals should participate in collective actions organized by civil society organizations, but the role and the normative status of CSOs are rarely discussed. In this paper, we argue that CSOs semi-perfect our shared obligation to address structural injustice by defining shared goals as well as taking actions to further them. This assigns a special moral status to CSOs, which in turn gives rise to our duty to support them. Thus, (...) we do not have full discretion when deciding whether to join collective actions or not. Under certain conditions, we can even be forced by others to do our share. (shrink)