Perhaps nowhere better than, "On the Names of God," can readers discern Laclau's appreciation of theology, specifically, negative theology, and the radical potencies of political theology. // It is Laclau's close attention to Eckhart and Dionysius in this essay that reveals a core theological strategy to be learned by populist reasons or social logics and applied in politics or democracies to come. // This mode of algorithmically informed negative political theology is not mathematically inert. It aspires to relate a fraction (...) or ratio to a series ... It strains to reduce the decided determinateness of such seriality ever condemned to the naive metaphysics of bad infinity. // It is worth considering that it is the specific 'number' of Dionysius in differential identification with an ineffable god (and, as such, a singular becoming between theology and numbers) that is floating in at least two dimensions [of signification] (be it political Demand on the horizontal dimension or theological Desire on [a] floating dimension) that cannot but *perform the link that relinks* names of god with any political life, populist reason, social justice, or radical democracy straining toward peace. (shrink)

In a recent contribution to this journal Paolo Gerbaudo has argued that an ‘elective affinity’ exists between social media and populism. The present article expands on Gerbaudo’s argument and examines various dimensions of this affinity in further detail. It argues that it is helpful to conceptually reframe the proposed affinity in terms of affordances. Four affordances are identified which make the social media ecology relatively favourable to both-right as well as left-wing populism, compared to the pre-social media ecology. (...) These affordances are neither stable nor uniquely fixed: they change in concordance with ongoing technological developments and in response to political events. Even though these dynamics can be quick-moving, a fairly stable alliance of interests between social media and populism seems to have emerged over the last decade. This raises the plausibility that as long as the current social media ecology persists, populist tendencies will remain prevalent in politics. (shrink)

Este trabalho pretende investigar como uma série de metamorfoses em nossos regimes de visibilidade constituem o que chamamos de populismo algorítmico e subjetivação ptolomaica. Recorremos, inicialmente, aos conceitos de disciplina, espetáculo e transparência – trabalhados respectivamente por Foucault, Debord e Han – para melhor compreendermos a formação de certos regimes de visibilidade e sua conexão com o modo de produção capitalista. Em seguida, trazemos o campo da tecnopolítica como superfície de inscrição privilegiada das novas formas de visibilidade e lançamos atenção (...) aos efeitos da filtragem algorítmica que produzem o que Pariser chamou de bolha-dos-filtros: a progressiva personalização dos conteúdos políticos, imagens e propagandas na internet. Argumentamos que esta ação de filtragem algorítmica está na base dos novos populismos, conforme as análises de Mouffe e Laclau. A partir de um sistema de equivalência de demandas insatisfeitas, as formas de laço social postas pelo atual populismo algorítmico se dão muito mais por uma frustração ou por um inimigo em comum do que por formas de idealização, tal qual nos mostravam a psicologia das massas analisada por Le Bon e Freud. Por fim, com o conceito de subjetivação ptolomaica, que consiste na produção tecnopolítica de um sujeito autocentrado e intolerante a tudo o que destoe do universo único a que o algoritmo deu exclusiva visibilidade, reencontramos os importantes temas do dogmatismo e do egocentrismo, trabalhados respectivamente por Kant e Piaget – o que, por sua vez, conecta o campo da Tecnopolítica ao campo da Filosofia da Educação no importante e atual tema das metamorfoses da visibilidade. (shrink)

Automated decision making for sentencing is the use of a software algorithm to analyse a convicted offender’s case and deliver a sentence. This chapter reviews the moral arguments for and against employing automated decision making for sentencing and finds that its use is in principle morally permissible. Specifically, it argues that well-designed automated decision making for sentencing will better approximate the just sentence than human sentencers. Moreover, it dismisses common concerns about transparency, privacy and bias as unpersuasive or inapplicable. The (...) chapter also notes that moral disagreement about theories of just sentencing are plausibly resolved by applying the principle of maximising expected moral choiceworthiness, and that automated decision making is better suited to the resulting ensemble model. Finally, the chapter considers the challenge posed by penal populism. The dispiriting conclusion is that although it is in theory morally desirable to use automated decision-making for criminal sentencing, it may well be the case that we ought not to try. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Editor’s Introduction: The State of Movement—or, Unassuming TheoryErik DoxtaderMotion [kinēsin], then, is both the same and not the same; we must admit that without boggling at it.—Xenos (the stranger), Plato’s SophistThe only answer is that we trace a path.—Walter Benjamin, “The Metaphysics of Youth”Are we there yet? Are we there yet? Are we there yet?—Lisa and Bart (from the backseat)The state of movement is a question—of movement, in theory.What (...) is movement, a movement, perhaps not least as the condition of arrival, an “original” beginning? Where does it come from? How does it work, in better and worse ways? To what does it lead—and why? If these standing questions remain open, there is also a chance that they are not questions at all, that they remain in some basic way beyond inquiry, precisely as they beg the question, as Giorgio Agamben has contended, of how “movement” remains “our unthought,” of the way in which “movement” presents us with the puzzle of an unconceived concept, the tension of a word whose work demands forgetting the “defeats and failures” of its use in the name and at the edges of democracy, and getting around the aporia of its necessary power without end (2005, 1). Perhaps we can only boggle—and perhaps we should. To inquire into the “state of movement” may be less a struggle for answers than the condition of question-ability itself, a movement of movement that appears in theory.Inspiring gesture. Endless stasis. Myriad advances. Countless retreats. Emerging hopes. Multiplying panics. Forced dislocation. Involuntary relocation. Indefinite incarceration. Sovereign and disciplinary borders crossed, closed, and blurred. Speech acts—in action. Moving words—gone [End Page 54] sideways. Gathering judgments. Calling out and compounding injustice. Cancelling the show. Incursions, attacks, invasions. History’s (always) incoming storm. Recalling, extending, and setting aside law’s precedent. Blown away, in a gust and a measure of time. Rising sea levels, receding forests, spiraling temperatures. Rustling aspen trees at altitude. Getting back on the bike. Staying put for the planet. Finding, instilling, and following desire. Unbounded discovery. Undue appropriation. Undoing what’s been done. Bodies at work, play, and ecstasy—and in decay, duress, and internment. Swept off the streets—and the quad. Vectors of transmission and expression. Breaking quarantine—and cliché. Soft landings and winding supply chains. Streaming words. Tropes turning into (intelligent) algorithms—and back again. Bullets flying... in homes, hospitals, classrooms. Struck by the light of a nebula and a sky full of kinetic kill vehicles. Populist uprising—progressive overreach. Equal and opposite reactions. Runway culture. Throwaway sociality. Publicity’s collapse. Privatization’s disclosure. Hopes for stillness and repose. Travel bans... for life. Packing the U-Haul for a better life. Generations letting go—and digging in. Rounds of chants. Days of marches. Cycles of emergency. Revolutionary aspirations in the avenues. Circling the leader, demanding commands. Running resistance. Caught out. Making way—and away.Asking after the state of movement may be less about the pause of cataloguing than the open that appears with being still, making a way of moving without movement, for a moment—to reflect on our understanding of the modes, manners, grammars, and vocabularies of movement and to speculate on the experience and so, in some basic sense, the assumption of movement, the line between those movements that remain in the background, out of view and taken for granted, often in the name of being able to simply get on with things, and those that provoke, invite, and disturb inquiry. If, for instance, the sort of movement named a “journey” is a long-standing and basic feature of the human condition (one can think variously, of better and much worse instantiations, from the Odyssey to the bloody quests for “salvation” that might have but mostly didn’t hinge on the air speed velocity of an unladen swallow to the Trail of Tears and Middle Passage to the moon shots), what’s happening in an American culture currently besotted with the idea of “being on a journey”—of discovery, fitness, creativity, acceptance, recovery, parenthood, leadership, home ownership, and so on and so forth. One wonders... (shrink)

Waged in 2016, Philippine President Rodrigo Duterte’s war on drugs has claimed over 20,000 lives according to human rights groups. The Duterte administration’s own count is significantly lower: around 6,000. The huge discrepancy between the government’s official count and that of arguably more impartial organizations about something as concretely material as body count is symptomatic of how disinformation is central to the Duterte administration and how it can sustain the approval of the majority of the Philippine electorate. We suggest that (...) Duterte’s populist politics generates what Boler and Davis call “affective feedback loops,” which create emotional and informational ecosystems that facilitate smooth algorithmic governance. We turn to Patron Saints of Nothing, a recently published novel by Randy Ribay about a Filipino-American who goes back to the Philippines to uncover the truth behind the death of his cousin. Jay’s journey into the “heart of darkness” as a “hyphenated” individual allows him access to locally networked subjectivities but not its affective entanglements. Throughout the novel, he encounters numerous versions of the circumstances of Jun’s demise and the truth remains elusive at the end of the novel. We argue that despite the constant distortion of fact and fiction in the novel, what remains relatively stable or “sticky” throughout the novel are the letters from Jun Reguero that Jay carries with him back to the Philippines. We suggest that these letters can potentially serve as a form of “dissensus” that challenges the constant redistribution of the sensible in the novel. (shrink)

This dissertation compares the notions of homo rationalis in Philosophy and homo oeconomicus in Economics. Particularly, in Part I, we claim that both notions are close methodological substitutes. Accordingly, we show that the constraints involved in the notion of economic rationality apply to the philosophical notion of rationality. On these premises, we explore the links between the notions of Kantian and Humean rationality in Philosophy and the constructivist and ecological approaches to rationality in economics, respectively. Particularly, we show that the (...) constraints involved in both approaches to rationality in economics apply to the notions of Kantian and Humean rationality. That is why we introduce the notion of minimal global rationality. In a nutshell, the latter links one’s rationality to one’s ability to make good use of their cognitive apparatus to process contextual variables. On this basis, in Part II, we present two empirical tests showing that minimal global rationality is at work in philosophical experiments because systematic manipulations of contextual variables predictably affect philosophical judgment in the Trolley Problem and in some Knobe-like vignettes. In the first case, the manipulation of the information provided to subjects predictably causes inconsistent judgments. More precisely, subjects’ response to information shocks predictably causes them to issue inconsistent judgments. Instead, in the second case, our experimental results reveal that the Knobe Effect activates only if the perpetrator of an action is likely to be perceived as evil and unfriendly. Besides, when the Knobe-like vignettes include probabilistic outcomes, the Knobe Effect does not activate or is reduced in magnitude. For these reasons, we maintain that the availability heuristic bias arguably affects one’s judgment in Knobe-like experiment settings. Eventually, in Part III, we survey the general implications of the findings presented in Part II. First and foremost, we show that when manned social media algorithms produce wide-ranging availability cascades, agents predictably respond to those cascades by herding around biased claims. Interestingly, we find that the exposure to similarly redundant online content makes new social groups emerge unhindered. That is why populists and conspiracy theorists obtain such a large follow-up online. Secondly, we find that manned social media algorithms can exploit the flaws of minimal global rationality at a faster pace than humans. Yet, given that latter algorithms cannot do so on their own, we argue that the Turing Test is still an open game. Specifically, we argue so because state-of-the-art technology can request a service to be supplied (e.g., Google Duplex) but cannot operate direct sales. This constraint of state-of-the-art technology is linked to its inability to exploit the flaws of global minimal rationality as well as humans can. Importantly, we show that if algorithms were able to do so, the involved sociopolitical risk would be high. Therefore, we argue that only some form of regulation over state-of-the-art technology can prevent and reduce such sociopolitical risks. -/- In case you want a copy of this dissertation, please email me. (shrink)

This chapter aims to direct attention to the political dimension of the social media age. Although current events like the Cambridge Analytica data breach managed to raise awareness for the issue, the systematically organized and orchestrated mechanisms at play still remain oblivious to most. Next to dangerous monopoly-tendencies among the powerful players on the market, reliance on automated algorithms in dealing with content seems to enable large-scale manipulation that is applied for economical and political purposes alike. The successful replacement of (...) traditional parties by movements based on personality cults around marketable young faces like Emmanuel Macron or Austria’s Sebastian Kurz is strongly linked to products and services offered by an industry that simply provides likes and followers for cash. Inspired by Trump’s monopolization of the Twitter-channel, these new political acteurs use the potential of social media for effective message control, allowing them to avoid confrontations with professional journalists. In addition, an extremely active minority of organized agitators relies on the viral potential of the web to strongly influence and dictate public discourse – suggesting a shift from the Spiral of Silence to the dangerous illusion of a Nexus of Noise. (shrink)

I consider statistical criteria of algorithmic fairness from the perspective of the _ideals_ of fairness to which these criteria are committed. I distinguish and describe three theoretical roles such ideals might play. The usefulness of this program is illustrated by taking Base Rate Tracking and its ratio variant as a case study. I identify and compare the ideals of these two criteria, then consider them in each of the aforementioned three roles for ideals. This ideals program may present a (...) way forward in the normative evaluation of candidate statistical criteria of algorithmic fairness. (shrink)

The potential for biases being built into algorithms has been known for some time, yet literature has only recently demonstrated the ways algorithmic profiling can result in social sorting and harm marginalised groups. We contend that with increased algorithmic complexity, biases will become more sophisticated and difficult to identify, control for, or contest. Our argument has four steps: first, we show how harnessing algorithms means that data gathered at a particular place and time relating to specific persons, can (...) be used to build group models applied in different contexts to different persons. Thus, privacy and data protection rights, with their focus on individuals, do not protect from the discriminatory potential of algorithmic profiling. Second, we explore the idea that anti-discrimination regulation may be more promising, but acknowledge limitations. Third, we argue that in order to harness anti-discrimination regulation, it needs to confront emergent forms of discrimination or risk creating new invisibilities, including invisibility from existing safeguards. Finally, we outline suggestions to address emergent forms of discrimination and exclusionary invisibilities via intersectional and post-colonial analysis. (shrink)

Marr's levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is (...) to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind's cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call “resource-rational analysis.”. (shrink)

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to (...) explain why this systemic exclusion is of moral concern and to offer a solution to address it. (shrink)

Algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current (...) empirical literature along four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making. (shrink)

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

This paper examines racial discrimination and algorithmic bias in predictive policing algorithms (PPAs), an emerging technology designed to predict threats and suggest solutions in law enforcement. We first describe what discrimination is in a case study of Chicago’s PPA. We then explain their causes with Broadbent’s contrastive model of causation and causal diagrams. Based on the cognitive science literature, we also explain why fairness is not an objective truth discoverable in laboratories but has context-sensitive social meanings that need to (...) be negotiated through democratic processes. With the above analysis, we next predict why some recommendations given in the bias reduction literature are not as effective as expected. Unlike the cliché highlighting equal participation for all stakeholders in predictive policing, we emphasize power structures to avoid hermeneutical lacunae. Finally, we aim to control PPA discrimination by proposing a governance solution—a framework of a social safety net. (shrink)

This contribution aims at proposing a framework for articulating different kinds of “normativities” that are and can be attributed to “algorithmic systems.” The technical normativity manifests itself through the lineage of technical objects. The norm expresses a technical scheme’s becoming as it mutates through, but also resists, inventions. The genealogy of neural networks shall provide a powerful illustration of this dynamic by engaging with their concrete functioning as well as their unsuspected potentialities. The socio-technical normativity accounts for the manners (...) in which engineers, as actors folded into socio-technical networks, willingly or unwittingly, infuse technical objects with values materialized in the system. Surveillance systems’ design will serve here to instantiate the ongoing mediation through which algorithmic systems are endowed with specific capacities. The behavioral normativity is the normative activity, in which both organic and mechanical behaviors are actively participating, undoing the identification of machines with “norm following,” and organisms with “norminstitution”. This proposition productively accounts for the singularity of machine learning algorithms, explored here through the case of recommender systems. The paper will provide substantial discussions of the notions of “normative” by cutting across history and philosophy of science, legal, and critical theory, as well as “algorithmics,” and by confronting our studies led in engineering laboratories with critical algorithm studies. (shrink)

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

The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within (...) organizations. We identify three key issues. First, we explore how algorithmic management shapes pre-existing power dynamics between workers and managers. Second, we discuss how algorithmic management demands new roles and competencies while also fostering oppositional attitudes toward algorithms. Third, we explain how algorithmic management impacts knowledge and information exchange within an organization, unpacking the concept of opacity on both a technical and organizational level. We conclude by situating this piece in broader discussions on the future of work, accountability, and identifying future research steps. (shrink)

We are currently witnessing a sharp rise in the use of algorithmic decision-making tools. In these instances, a new wave of policy concerns is set forth. This article strives to map out these issues, separating the wheat from the chaff. It aims to provide policy makers and scholars with a comprehensive framework for approaching these thorny issues in their various capacities. To achieve this objective, this article focuses its attention on a general analytical framework, which will be applied to (...) a specific subset of the overall discussion. The analytical framework will reduce the discussion to two dimensions, every one of which addressing two central elements. These four factors call for a distinct discussion, which is at times absent in the existing literature. The two dimensions are the specific and novel problems the process assumedly generates and the specific attributes which exacerbate them. While the problems are articulated in a variety of ways, they most likely could be reduced to two broad categories: efficiency and fairness-based concerns. In the context of this discussion, such problems are usually linked to two salient attributes the algorithmic processes feature—its opaque and automated nature. (shrink)

We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and (...) argue that at least some regulatory proposals for explainable AI could end up setting the bar higher than is necessary or indeed helpful. The demands of practical reason require the justification of action to be pitched at the level of practical reason. Decision tools that support or supplant practical reasoning should not be expected to aim higher than this. We cast this desideratum in terms of Daniel Dennett’s theory of the “intentional stance” and argue that since the justification of action for human purposes takes the form of intentional stance explanation, the justification of algorithmic decisions should take the same form. In practice, this means that the sorts of explanations for algorithmic decisions that are analogous to intentional stance explanations should be preferred over ones that aim at the architectural innards of a decision tool. (shrink)

Recent technological and methodological changes in farming have led to an emerging set of claims about the role of digital technology in food production. Known as precision agriculture, the integration of digital management and surveillance technologies in farming is normatively presented as a revolutionary transformation. Proponents contend that machine learning, Big Data, and automation will create more accurate, efficient, transparent, and environmentally friendly food production, staving off both food insecurity and ecological ruin. This article contributes a critique of these rhetorical (...) and discursive claims to a growing body of critical literature on precision agriculture. It argues precision agriculture is less a revolution than an evolution, an effort to shore up and intensify the conventional farming system responsible for generating many of the social and environmental problems precision agriculture is presented as solving. While precision agriculture advocates portray it as a radical, even democratic epistemological break with the past, this paper locates truth claims surrounding datafication and algorithmic control in farming within deeper historical contexts of the capitalist rationalization of production and efforts to quantify and automate physical and mental labor. Abjuring the growing cultural tendency to treat algorithmic systems as revolutionary in favor of social and historical dimensions of precision agriculture, can help re-frame the discussion about its design and use around real, socially and ecologically oriented change in farming, and so ensure that the possibilities and benefits of precision agriculture are as evenly and effectively shared as possible. (shrink)

With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be _fair_. Rather they express (...) different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account. (shrink)

As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. (...) We counter by pointing out important inaccuracies about the science and rejecting the apparent theory-first focus. More broadly, the case study reveals significant limitations of the predominant epistemic-semantic conceptions of scientific progress and the considerable merits of pragmatic, practically-oriented accounts. (shrink)

Recommender systems are artificial intelligence technologies, deployed by online platforms, that model our individual preferences and direct our attention to content we’re likely to engage with. As the digital world has become increasingly saturated with information, we’ve become ever more reliant on these tools to efficiently allocate our attention. And our reliance on algorithmic recommendation may, in turn, reshape us as moral agents. While recommender systems could in principle enhance our moral agency by enabling us to cut through the (...) information saturation of the internet and focus on things that matter, as they’re currently designed and implemented they’re apt to interfere with our ability to attend appropriately to morally relevant factors. In order to analyze the distinctive moral problems algorithmic recommendation poses, we develop a framework for the ethics of attention and an account of judicious attention allocation as a moral skill. We then discuss empirical evidence suggesting that attentional moral skill can be thwarted and undermined in various ways by algorithmic recommendation and related affordances of online platforms, as well as economic and technical considerations that support this concern. Finally, we consider how emerging technologies might overcome the problems we identify. (shrink)

The present paper is devoted to computational aspects of propositional inconsistency-adaptive logics. In particular, we prove (relativized versions of) some principal results on computational complexity of derivability in such logics, namely in cases of CLuN r and CLuN m , i.e., CLuN supplied with the reliability strategy and the minimal abnormality strategy, respectively.

Holm (2022) argues that a class of algorithmic fairness measures, that he refers to as the ‘performance parity criteria’, can be understood as applications of John Broome’s Fairness Principle. We argue that the performance parity criteria cannot be read this way. This is because in the relevant context, the Fairness Principle requires the equalization of actual individuals’ individual-level chances of obtaining some good (such as an accurate prediction from a predictive system), but the performance parity criteria do not guarantee (...) any such thing: the measures merely ensure that certain population-level ratios hold. (shrink)

What does it mean for algorithmic classifications to be fair to different socially salient groups? According to classification parity criteria, what is required is equality across groups with respect to some performance measure such as error rates. Critics of classification parity object that classification parity entails that achieving fairness may require us to choose an algorithm that makes no group better off and some groups worse off than an alternative. In this article, I interpret the problem of algorithmic (...) fairness as a case concerning the ethics of the distribution of algorithmic classifications across groups (as opposed to, e.g., the fairness of data collection). I begin with a short introduction of algorithmic fairness as a problem discussed in machine learning. I then show how the criticism raised against classification parity is a form of leveling down objection, and I interpret the egalitarianism of classification parity as deontic egalitarianism. I then discuss a challenge to this interpretation and suggest a revision. Finally, I examine how my interpretation provides proponents of classification parity with a response to the leveling down criticism and how it relates to a recent suggestion to evaluate fairness for automated decision-making systems based on risk and welfare considerations from behind a veil of ignorance. (shrink)

This paper examines folk theories of algorithmic recommendations on Spotify in order to make visible the cultural specificities of data assemblages in the global South. The study was conducted in Costa Rica and draws on triangulated data from 30 interviews, 4 focus groups with 22 users, and the study of “rich pictures” made by individuals to graphically represent their understanding of algorithmic recommendations. We found two main folk theories: one that personifies Spotify and another one that envisions it (...) as a system full of resources. Whereas the first theory emphasizes local conceptions of social relations to make sense of algorithms, the second one stresses the role of algorithms in providing a global experience of music and technology. We analyze why people espouse either one of these theories and how these theories provide users with resources to enact different modalities of power and resistance in relation to recommendation algorithms. We argue that folk theories thus offer a productive way to broaden understanding of what agency means in relation to algorithms. (shrink)

Algorithmic discrimination has rapidly become a topic of intense public and academic interest. This article explores three issues raised by algorithmic discrimination: 1) the distinction between direct and indirect discrimination, 2) the notion of disadvantageous treatment, and 3) the moral badness of discriminatory automated decision-making. It argues that some conventional distinctions between direct and indirect discrimination appear not to apply to algorithmic discrimination, that algorithmic discrimination may often be discrimination between groups, as opposed to against groups, (...) and that it is not necessarily the case that morally bad algorithmic discrimination gives us reason to not use automated decision-making. For each of the three issues, the article explores implications for algorithmic discrimination, suggests some alternative answers, and clarifies how we may want to think of discrimination more broadly in light of lessons drawn from the context of algorithmic discrimination. (shrink)

The application of algorithms in organizations is becoming more widespread. Previous research has aimed to enhance employees’ perceptions of algorithmic fairness by focusing on technical features. However, individuals often struggle to observe and comprehend these features, hindering their ability to form rational fairness judgments. Drawing upon fairness heuristic theory, this study explores how individuals perceive algorithmic fairness when technical features are invisible because of algorithmic opacity. Research conducted with food delivery riders in China suggests that, in the (...) absence of transparent algorithmic information, riders heuristically form perceptions of algorithmic fairness based on more salient and accessible distributive fairness information. These heuristic perceptions of algorithmic fairness further predict outcomes such as task performance and helping behavior. We also found that different distributive fairness information holds varying importance in the process of shaping heuristic perceptions of algorithmic fairness and that algorithmic transparency perceptions and tenure moderate this process. The findings extend fairness heuristic theory and have practical implications. (shrink)

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, arguably, to the (...) lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

The problem of algorithmic fairness is typically framed as the problem of finding a unique formal criterion that guarantees that a given algorithmic decision-making procedure is morally permissible. In this paper, I argue that this is conceptually misguided and that we should replace the problem with two sub-problems. If we examine how most state-of-the-art machine learning systems work, we notice that there are two distinct stages in the decision-making process. First, a prediction of a relevant property is made. (...) Secondly, a decision is taken based (at least partly) on this prediction. These two stages have different aims: the prediction is aimed at accuracy, while the decision is aimed at allocating a given good in a way that maximizes some context-relative utility measure. Correspondingly, two different fairness issues can arise. First, predictions could be biased in discriminatory ways. This means that the predictions contain systematic errors for a specific group of individuals. Secondly, the system’s decisions could result in an allocation of goods that is in tension with the principles of distributive justice. These two fairness issues are distinct problems that require different types of solutions. I here provide a formal framework to address both issues and argue that this way of conceptualizing them resolves some of the paradoxes present in the discussion of algorithmic fairness. (shrink)

A growing literature is taking an institutionalist and governance perspective on how algorithms shape society based on unprecedented capacities for managing social complexity. Algorithmic governance altogether emerges as a novel and distinctive kind of societal steering. It appears to transcend established categories and modes of governance—and thus seems to call for new ways of thinking about how social relations can be regulated and ordered. However, as this paper argues, despite its novel way of realizing outcomes of collective steering and (...) coordination, it can nevertheless be grasped with an old and fundamental figure in political philosophy: that of Thomas Hobbes’ Leviathan. Comparing algorithmic governance with this figure serves to highlight their similarities as socio-political arrangements, and specifically to clarify how algorithmic governance parallels the apolitical traits of the Leviathan—it eliminates the political as it requires compliance and forgoing contestation to best fulfill its role and to produce satisfying outcomes. (shrink)

Recently, there has been much discussion of ‘fair machine learning’: fairness in data-driven decision-making systems (which are often, though not always, made with assistance from machine learning systems). Notorious impossibility results show that we cannot have everything we want here. Such problems call for careful thinking about the foundations of fair machine learning. Sune Holm has identified one promising way forward, which involves applying John Broome's theory of fairness to the puzzles of fair machine learning. Unfortunately, his application of Broome's (...) theory appears to be fatally flawed. This article attempts to rescue Holm's central insight – namely, that Broome's theory can be useful to the study of fair machine learning – by giving an alternative application of Broome's theory, which involves thinking about fair machine learning in counterfactual (as opposed to merely statistical) terms. (shrink)

This paper examines the development and technological mediation of the concept of solidarity. We focus on the workplace as a focal point of solidarity relations, and utilise a phenomenological approach to describe and analyse those relations. Workplace solidarity, which has been historically concretised through social objects such as labor unions, is of particular political relevance since it has played an outsize role in the broader struggle for social, economic, and political rights, recognition, and equality. We argue that the use of (...) automated decision support systems in labor process management may negatively affect the formation of these relations. As solidarity motivates collective political action and risk-taking, the mediation and potential obstruction of solidarity relations by ADS is politically significant. We contribute to the growing literature on the “future of work” problem in elucidating the technological mediation of workplace solidarity. (shrink)

Algorithmic decision-making systems have the potential to amplify existing discriminatory patterns and negatively affect perceptions of justice in society. There is a need for a revision of mechanisms to address discrimination in light of the unique challenges presented by these systems, which are not easily auditable or explainable. Research efforts to bring fairness to ADM solutions should be viewed as a matter of justice and trust among actors should be ensured through technology design. Ideas that move us to explore (...) the notions of justice within the field of political thinking, aiming to identify the elements that frame the social context of the discriminatory decisions produced by algorithmic decision-making solutions. Our explorations suggest that the efforts to bring fairness to ADMs should be seen as a matter of justice and that trust in these systems can be ensured through careful technology design. (shrink)

The Passenger Name Record (PNR) Directive has introduced a pre-emptive, risk-based approach in the landscape of European databases and information exchange for security purposes. The article contributes to ongoing debates on algorithmic security and data-driven decision-making by fleshing out the specific way in which the EU PNR-based approach to security substantiates core characteristics of algorithmic regulation. The EU PNR framework appropriates data produced in the commercial sector for generating security-related behavioural predictions and does so in a way that (...) gives rise to a paradoxical normativity directly dependent on empirical states. Its ‘securitisation move’ is moreover characterised by an inherent tendence to expand. As a result, the PNR Directive poses challenges for existing check and balance mechanisms and for human autonomy. These challenges could be partially addressed by strengthening ex-post control procedures and independent auditing. Yet in the decision to adopt a risk-based security model, something more fundamental seems to be at stake, namely, the preservation of the idea of human beings as moral agents able to direct and modify their behaviour in accordance with an intelligible, reliable and predictable normative order. (shrink)

We prove various results connected together by the common thread of computability theory.First, we investigate a new notion of algorithmic dimension, the inescapable dimension, which lies between the effective Hausdorff and packing dimensions. We also study its generalizations, obtaining an embedding of the Turing degrees into notions of dimension.We then investigate a new notion of computability theoretic immunity that arose in the course of the previous study, that of a set of natural numbers with no co-enumerable subsets. We demonstrate (...) how this notion of $\Pi ^0_1$ -immunity is connected to other immunity notions, and construct $\Pi ^0_1$ -immune reals throughout the high/low and Ershov hierarchies. We also study those degrees that cannot compute or cannot co-enumerate a $\Pi ^0_1$ -immune set.Finally, we discuss a recently discovered truth-table reduction for transforming a Kolmogorov–Loveland random input into a Martin-Löf random output by exploiting the fact that at least one half of such a KL-random is itself ML-random. We show that there is no better algorithm relying on this fact, in the sense that there is no positive, linear, or bounded truth-table reduction which does this. We also generalize these results to the problem of outputting randomness from infinitely many inputs, only some of which are random.Abstract prepared by David J. Webb.E-mail: [email protected]: https://arxiv.org/pdf/2209.05659.pdf. (shrink)

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and (...) erroneous treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine. (shrink)

Critical algorithm scholarship has demonstrated the difficulties of attributing accountability for the actions and effects of algorithmic systems. In this commentary, we argue that we cannot stop at denouncing the lack of accountability for algorithms and their effects but must engage the broader systems and distributed agencies that algorithmic systems exist within; including standards, regulations, technologies, and social relations. To this end, we explore accountability in “the Generated Detective,” an algorithmically generated comic. Taking up the mantle of detectives (...) ourselves, we investigate accountability in relation to this piece of experimental fiction. We problematize efforts to effect accountability through transparency by undertaking a simple operation: asking for permission to re-publish a set of the algorithmically selected and modified words and images which make the frames of the comic. Recounting this process, we demonstrate slippage between the “complication” of the algorithm and the obscurity of the legal and institutional structures in which it exists. (shrink)