The call to make artificial intelligence (AI) more democratic, or to “democratize AI,” is sometimes framed as a promising response for mitigating algorithmic injustice or making AI more aligned with social justice. However, the notion of “democratizing AI” is elusive, as the phrase has been associated with multiple meanings and practices, and the extent to which it may help mitigate algorithmic injustice is still underexplored. In this paper, based on a socio-technical understanding of algorithmic (...) class='Hi'>injustice, I examine three notable notions of democratizing AI and their associated measures—democratizing AI use, democratizing AI development, and democratizing AI governance—regarding their respective prospects and limits in response to algorithmic injustice. My examinations reveal that while some versions of democratizing AI bear the prospect of mitigating the concern of algorithmic injustice, others are somewhat limited and might even function to perpetuate unjust power hierarchies. This analysis thus urges a more fine-grained discussion on how to democratize AI and suggests that closer scrutiny of the power dynamics embedded in the socio-technical structure can help guide such explorations. (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)

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)

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)

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)

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)

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)

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)

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)

Dwork, Hardt, Pitassi, Reingold, & Zemel [6] introduced two notions of fairness, each of which is meant to formalize the notion of similar treatment for similarly qualified individuals. The first of these notions, which we call additive metric fairness, has received much attention in subsequent work studying the fairness of a system composed of classifiers which are fair when considered in isolation [3, 4, 7, 8, 12] and in work studying the relationship between fair treatment of individuals and fair treatment (...) of groups [6, 7, 13]. Here, we extend these lines of research to the second, less-studied notion, which we call multiplicative metric fairness. In particular, we exactly characterize the fairness of conjunctions and disjunctions of multiplicative metric fair classifiers, and the extent to which a classifier which satisfies multiplicative metric fairness also treats groups fairly. This characterization reveals that whereas additive metric fairness becomes easier to satisfy when probabilities of acceptance are small, leading to unfairness under functional and group compositions, multiplicative metric fairness is better-behaved, due to its scale-invariance. (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)

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)

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)

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.

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)

In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their autonomy based theory. (...) Most notably, proponents of consequentialism and virtue ethics can appropriate RCPs insights. This is good news because RCP’s social contract theory is vulnerable to a well known class of counterexamples. The most significant contribution of RCP, if I am right, is in their identification, presentation, and evaluation of concrete cases involving algorithms and not in the more controversial claims about theoretical ethics that RCP themselves see as central to what they are doing. (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)

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)

How might people be wronged in relation to fear? Recently philosophers have begun to investigate the idea that there may be distinctly affective forms of injustice (Archer & Mills 2019; Archer & Matheson 2022; Gallegos 2022; Srinivasan 2018; Whitney 2018). Until now, though, the literature on affective injustice has mostly focused on the emotion of anger. Similarly, while philosophers have investigated both ethical (Döring 2020; Harbin 2023) and political (Ahmed 2004; Nussbaum 2019) questions related to fear, this literature (...) has not yet drawn on the literature of affective injustice to investigate these issues. We will investigate how fear can be a site of affective injustice. We will outline three forms of affective injustice involving fear. We begin by providing an overview of the existing literature on affective injustice. We will then argue that the unfair dismissal of people’s fears can constitute a form of affective injustice. Next, we will argue that being made to live in fear can also constitute a form of affective injustice. Finally, we will outline the last form of fear-related affective injustice, which is being the target of unwarranted fear. In exploring these various forms of fear-related affective injustice we will highlight more general features of affective injustice that have hitherto been underacknowledged. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will (...) call the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays (...) considers different dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues. (shrink)

Implementing an algorithm on part of our causally-interconnected physical environment requires three choices that are typically considered arbitrary, i.e. no single option is innately privileged without invoking an external observer perspective. First, how to delineate one set of local causal relationships from the environment. Second, within this delineation, which inputs and outputs to designate for attention. Third, what meaning to assign to particular states of the designated inputs and outputs. Having explained these types of arbitrariness, we assess their relevance for (...) algorithms from various computational theories of mind (CTM) seeking to account for phenomenal consciousness. Some CTM accounts can survive some of these arbitrariness challenges, but eventually each one examined faces a trilemma. Each is under pressure to dilute at least one of three important CTM desiderata: its causal relevance, its substrate neutrality, or its assignment discrimination (e.g. ability to avoid near-panpsychism). However, unlike previous research, we suggest the debate cannot be fully settled at the level of principle. Certain complex CTM algorithms may be salvageable, albeit not the algorithms specified to date. The paper specifies three areas of investigative work, as well as further theoretical avenues of research, which computationalists may wish to explore to escape the trilemma. (shrink)

Advocates of remedying historical injustices urge political communities to take responsibility for their past, but their arguments are ambiguous about whether all past injustices need remedy, or just those regarding groups that suffer from current injustice. This ambiguity leaves unanswered the challenge of critics who argue that contemporary injustices matter, not those in the past. I argue instead for a focus on injustices that have roots in the past, and continue to the present day, what I call enduring (...) class='Hi'>injustice. Instead of focusing on finding the party responsible for the injustice, I argue that we use history to help us understand why some injustices endure, which I suggest is partly due to the limitations of liberal justice. I conclude with a conception of responsibility for repairing enduring injustice that deemphasizes searching for the causal agent, and instead focuses on how to repair the injustice, which I explain through an expansive conception of shared space. (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)

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 paper, I present an analysis of the “windows into reality” that are used in theories of global justice with a focus on issues of epistemic injustice and the powerlessness of the global poor. I argue that we should aim for a better understanding of global poverty through acknowledging people living in poverty as epistemic subjects. To achieve this, we need to deepen and broaden the knowledge base of theories of global justice and approach the subject through methodologies (...) of “thinking small” and “thick descriptions”, which are ways to give people living in poverty sufficient room to express themselves and have their voices heard, leading to “small” and “thick” knowledge claims. (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)

Epistemic injustice has become one of the most widely discussed topics in social epistemology, and has revived interest in issues in the intersections between epistemology and ethics and political philosophy. Much of the impetus for this recent explosion of interest has been the influential work of Miranda Fricker; however, Fricker’s framework and terminology for discussing the phenomena and the kinds of examples she’s interested in has not always been cleanly separated from the phenomena themselves. This chapter examines what’s distinctive (...) of Fricker’s treatment of the phenomena she labels epistemic injustice, focusing on the two varieties that her discussion highlights, testimonial injustice and hermeneutical injustice. Once the distinctive elements of Fricker’s treatment are clearer, we will be better placed to assess which of them are plausible and which implausible, and to consider alternative theoretical frameworks for thinking about the phenomena, such as that developed by Kristie Dotson. (shrink)

Psychiatric language and concepts, and the norms they embed, have come to influence more and more areas of our daily lives. This has recently been described as a feature of the ‘psychiatrization of society.’ This paper looks at one aspect of psychiatrization that is still little studied in the literature: the psychiatrization of our emotional lives. The paper develops an extended account of emotion pathologizing as a form of affective injustice that is related to psychiatrization and that specifically harms (...) psychopathologized people, i.e., people who are socially perceived to be mentally ill. After introducing an initial account of emotion pathologizing as articulated in Pismenny et al. (2024), we extend the account by demonstrating how processes and practices of emotion pathologizing are informed by (1) the dominant biomedical approach to psychiatry and (2) sanism, a system of discrimination and oppression that disadvantages people who have received a psychiatric diagnosis, or are perceived as in need of psychiatric treatment. We then argue that emotion pathologizing can manifest as an affect-related hermeneutical injustice that disadvantages psychopathologized individuals by unfairly constraining how they make sense of and understand their own emotional experiences. (shrink)

In this paper, I argue that a special kind of hermeneutical injustice occurs when someone is not permitted to interpret their experiences in a meaning-making way. I suggest that this occurs in certain cases where the possibility that the patient has a genuine religious experience is excluded by a medical diagnosis. In such cases, it is not that an experience is incomprehensible because of the absence of a valid interpretation. Instead, one perspective is not only dominant but exclusive, so (...) the ensuing power imbalance is absolute. I begin by introducing the notion of hermeneutical injustice and subsequent refinements that have been instrumental in applying it to psychiatry. Then I show how even though voice-hearers are liable to be victims of such injustices, the literature has neglected the specific harm done to someone whose interpretation of their own experience is obliterated because of the dominance of exclusive medical views. To illustrate this, I point to a case that evinces the harms perpetrated toward patients diagnosed with psychotic symptoms with religious content and argue that depriving persons of meaning-making interpretations of their own experiences constitutes a profound and willful form of hermeneutical injustice. I then gesture toward studies on the religious reframing of anomalous experiences through contact with a normalizing framework. Finally, I pry from a successful case a general directive toward the virtue of hermeneutical justice through the attitudes of respect to first-person authority and the hermeneutical flexibility it embodies. (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)

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)

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)

Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted, the information we see online. 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 been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic (...) bias? (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)

This paper explores the issue of epistemic injustice in research evaluation. Through an analysis of the disciplinary cultures of physics and humanities, we attempt to identify some aims and values specific to the disciplinary areas. We suggest that credibility is at stake when the cultural values and goals of a discipline contradict those presupposed by official evaluation standards. Disciplines that are better aligned with the epistemic assumptions of evaluation standards appear to produce more "scientific" findings. To restore epistemic justice (...) in research evaluation, we argue that the specificity of a discipline's epistemic aims, values, and cultural identities must be taken into account. (shrink)

There are many situations and policies that strike us as unjust and make us look for alternatives. Yet in the absence of a clear definition, we may end up by equating injustice with everything that is evil in the world.

Algorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents (...) to act faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives. (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)

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)

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)

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)

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)

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)

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)

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)