This paper concerns the role of the unconscious in technology. The central thesis is that there exists an experience of non-acceptance and failed incorporation of technology, which (a) does not depend on the technical engineering dimension of the artifact but (b) instead concerns the relationship between the human unconscious and the artifact. This thesis is supported and developed through the analysis of a case study – that is, the creation and development of the first version of Google Glass. (...) The failure of the first version of Google Glass is explained in terms of the non-acceptance of this technology. This paper presents an analysis of users’ experiences and comments as a basis for interpreting the relationship between the unconscious and technology. Their non-acceptance of this technology is explained and further clarified from a postphenomenological point of view, utilizing the concept of “technological uncanny.” The analysis suggests that antimediation can be understood as a form of noise. In fact, as non-acceptance, antimediation directly concerns the relationship between contingency and control. (shrink)

Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries (...) and public policies that can detect or reduce the likelihood of racial bias in algorithmic design and execution. Design/methodology/approach The paper shares examples in the US where algorithmic biases have been reported and the strategies for explaining and addressing them. Findings The findings of the paper suggest that explicit racial bias in algorithms can be mitigated by existing laws, including those governing housing, employment, and the extension of credit. Implicit, or unconscious, biases are harder to redress without more diverse workplaces and public policies that have an approach to bias detection and mitigation. Research limitations/implications The major implication of this research is that further research needs to be done. Increasing the scholarly research in this area will be a major contribution in understanding how emerging technologies are creating disparate and unfair treatment for certain populations. Practical implications The practical implications of the work point to areas within industries and the government that can tackle the question of algorithmic bias, fairness and accountability, especially African-Americans. Social implications The social implications are that emerging technologies are not devoid of societal influences that constantly define positions of power, values, and norms. Originality/value The paper joins a scarcity of existing research, especially in the area that intersects race and algorithmic development. (shrink)

This paper delves into the nuances of ‘fashion’ in recommender systems and social media analytics, which shape and define an individual’s perception and self-relationality. Its aim is twofold: first, it supports a different perspective on privacy that focuses on the individual’s process of identity construction considering the social and personal aspects of ‘fashion’. Second, it underlines the limitations of computational models in capturing the diverse meaning of ‘fashion’, whereby the algorithmic prediction of user preferences is based on individual conscious (...) and unconscious associations with fashion identity. I test both of these claims in the context of current concerns over the impact of algorithmic personalisation systems on individual autonomy and privacy: creating ‘filter bubbles’, nudging the user beyond their conscious awareness, as well as the inherent bias in algorithmic decision-making. We need an understanding of privacy that sustains the inherent reduction of fashion identity to literal attributes and protects individual autonomy in shaping algorithmic approximations of the self. (shrink)

The computational approach to psychiatric disorders, including delusions, promises explanation and treatment. Here, we argue that an information processing approach might be misleading to understand psychopathology and requires further refinement. We explore the claim of computational psychiatry being a bridge between phenomenology and physiology while focussing on the ontological commitments and corresponding methodology computational psychiatry is based on. Interconnecting ontological claims and methodological practices, the paper illustrates the structure of theory-building and testing in computational psychiatry.First, we will explain the ontological (...) commitments computational psychiatry is grounded in, theBayesian Brain hypothesis(BBH) of unconscious inference, paired with normative deontic approaches applied to gauge psychopathology. We then turn to the steps taken in empirical paradigms, from definitions, which are used as starting points, to the operationalisation and isolation of cognitive processes and hypothesis testing based on algorithmic models, to consecutive interpretations regarding the aetiology of psychiatric disorders. We outline how experimental paradigms in computational psychiatry are specifically designed to confirm aberrations in assumed inferential processes, which are thought of as being the underlyingcore invariant features.We will illustrate a gap between the ontological commitments of computational psychiatry and the operationalisation and testing of the cognition assumed to be relevant for psychopathology. This conceptual gap is of utmost importance when designing computational paradigms and may impede a crisp understanding of the approach. Lastly, in evaluating the conceptual gap, it becomes apparent that the information processing formalism used in computational psychiatry is still grounded in rational cognitive psychology. (shrink)

Could a computer be programmed to make moral judgments about cases of intentional harm and unreasonable risk that match those judgments people already make intuitively? If the human moral sense is an unconscious computational mechanism of some sort, as many cognitive scientists have suggested, then the answer should be yes. So too if the search for reflective equilibrium is a sound enterprise, since achieving this state of affairs requires demarcating a set of considered judgments, stating them as explanandum sentences, (...) and formulating a set of algorithms from which they can be derived. The same is true for theories that emphasize the role of emotions or heuristics in moral cognition, since they ultimately depend on intuitive appraisals of the stimulus that accomplish essentially the same tasks. Drawing on deontic logic, action theory, moral philosophy, and the common law of tort, particularly Terry's five-variable calculus of risk, I outline a formal model of moral grammar and intuitive jurisprudence along the foregoing lines, which defines the abstract properties of the relevant mapping and demonstrates their descriptive adequacy with respect to a range of common moral intuitions, which experimental studies have suggested may be universal or nearly so. Framing effects, protected values, and implications for the neuroscience of moral intuition are also discussed. (shrink)

The article aims to map the contemporary techno-social networks, together with delineation of the algorithmic governmentality, computational unconscious, the epistemic structure of the Eurocentric matrix of power haunted by its own repetition of the constant abyss of horrors, only to search for gestures of resistance. Gestures of resistance, contrary to the false conviction of capitalist realism, can be found everywhere, including in Jordan Peele's Nope (2022). Through a variety of motifs, themes, and cultural and cinematic references, Peele creates (...) a resistance image, i.e., an image that resists the historical trajectory of the violence of the digital colonial matrix of knowledge. In particular with Nope, in which the history of racial violence is disentangled by evoking the relation between the entanglement of capital and epistemic violence embodied in an all-devouring predator UFO. But Nope is also about visualizing silenced histories. Indeed, to strive to capture UFO with the camera is to break away from modernity as a totalizing onto-epistemology and in this register generating a false universal subject of a Man. (shrink)

The text deals with the recently renewed issue of?antiphilosophy? in the self-understanding of some prominent contemporary continental philosophers but not only them, such as Alain Badiou and Slavoj Zizek, both referring to the psychoanalyst and theoretician of discourse Jacques Lacan. Starting with a metaphorical analysis of the verdict made by Marx of?merely interpretive? character of philosophy in relation to?the study of real world? and with his comparison of philosophy with?masturbation?, the text addresses new appeals to?antiphilosophy? as samples of a token (...) or rather, as cases of a syndrome connected with Jocasta?s rationalization of Oedipus? curse. It was her discourse based on general rules of rationality with which she unconsciously attempted to avoid the confrontation with the truth of an event which had already occurred. In the text, presuppositions and consequences of such an analogy between ancient and contemporary philosophical material are discussed in order to show-on the basis of arguing that the unconscious is located within rational processes that subjects undertake in order to respond to rational needs -that contemporary discussion about?antiphilosophy? by left-wing philosophers bears unacknowledged tendencies to unconsciously reduce Marx? algorithm of?realization of philosophy? to the Hegelian negation of the negation and to integrate it into the discourse of academic philosophy. Tema teksta je nedavno iznova pokrenuto pitanje "antifilozofije" u samorazumijevanju nekih prominentnih suvremenih evropskih filozofa, ali ne samo njih, kao sto su Alain Badiou i Slavoj Zizek koji se obojica pozivaju na psihoanaliticara i teoreticara diskursa Jacquesa Lacana. Polazeci od metaforicke analize Marxovog iskaza o "puko interpretativnom" karakteru filozofije u odnosu na "studij stvarnog zivota" i od njegove usporedbe filozofije s masturbacijom, tekst se obraca novim pozivanjima na "antifilozofiju" tretirajuci ih kao primjerke jednog tipa, ili radije kao slucajeve jednog sindroma, povezanog s Jokastinom racionalizacijom Edipovog prokletstva; njezin diskurs utemeljen na opcim pravilima racionalnosti nesvjesni je pokusaj izbjegavanja susreta s istinom dogadjaja koji se vec dogodio. U tekstu se ispituju pretpostavke i posljedice takve analogije izmedju antickog i suvremenog tekstualnog materijala s ciljem da se pokaze - na temelju argumenta da je nesvjesno locirano unutar racionalnih procesa koje subjekt poduzima da bi udovoljio racionalnim potrebama - da suvremeno razumijevanje "antifilozofije" nosi u sebi neprepoznate tendencije na lijevom krilu suvremene filozofije da se Marxov algoritam ozbiljenja filozofije reducira na Hegelovu negaciju negacije i integrira u diskurs akademske filozofije. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that connect human (...) brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

According to the 2020 docudrama, The Social Dilemma, our very addiction to “social media” has, today, become encapsulated in the tensions between its facilitation as a mode of interpersonal communication and as an insidious conduit for machine learning, surveillance capitalism and manipulation. Amidst a variety of interviewees – many of whom are former employees of social media companies – the documentary finishes on a unanimous conclusion: something must change. By using the docudrama as a pertinent example of our “social media (...) malaise,” and while remaining aware of the problems and unethical practices encompassing international digital/social media companies, this paper will argue that we continually refrain from the very question(ing) that would call these companies to account: what does the algorithm desire? In approaching this question, this article will draw from Lacan’s ‘hysterical’ position in accordance with Robert Pfaller’s notion of interpassivity. Together, these concepts will be used to provide a psychoanalytic account of how our subjectivization in social media renders an unconscious endorsement that both frames our awareness of the dilemmas encompassing social media, while also positing an inherent limitation that may offer a possible path out of its impeding affects. This subjective ambivalence – delegated yet reluctantly disavowed – offers an opportunity to realign discussions on the lost object of desire (objet a) and its reproduction in social media algorithms. In so doing, the case will be made that an account of interpassivity can help lay bare the hysterical significance underscoring our digital subjectivization. (shrink)

The article aims to demonstrate that the nudge theory suffers from three main weaknesses stemming from its theoretical dependence on behavioural economics. The first two weaknesses endanger the paternalistic goal, whereas the third does not justify the libertarian attribute. The first weakness lies in the incomplete realistic characterisation of behavioural economics theory that is the central theoretical pillar of Nudge theory. The second weakness is even more relevant. The normative model of behavioural economics is neoclassical rationality. It can be applied (...) to choices in conditions of risk, when it is possible to forecast the probability of the outcomes of one’s choices. On the contrary the real life problems are inside a complex and uncertain environment. In an uncertain environment the neoclassical algorithms are no longer valid, in that they postulate the knowledge of the probability of the outputs. Thus errors and bias, as observed in behavioural economics, are not such in many instances, but they are instead adaptive and therefore rational responses to the context of decision-making. Therefore nudge theory that pursues the normative paternalistic objective of neutralising the bias and judgement errors discovered by behavioural economics and putting paternalistically the citizen on the right track, is in fact proceeding in the wrong direction. The last weakness concerns an assumption of Nudge theory about the duality of mind. System 1 represents the intuitive mind, while System 2 corresponds to the analytical one. System 1 tends to make decisions quickly and unconsciously, while System 2 follows a slow and conscious process. Biases and judgement errors are mainly generated by System 1. However, System 2 may later intervene to correct them. From this premise, the Nudge theory affirms its libertarian character. In this paper I will argue that the dualism of the mind would not entail the reversibility of some nudges, particularly of default states. In this way the fundamental pillar is weakened to justify the libertarian character of the nudge theory. (shrink)

Certain strands of contemporary media theory are concerned with the ways in which computational environments exploit the ‘missing half-second’ of human perception and thereby influence, control or exploit humans at an affective level. The ‘technological unconscious’ of our times is often understood to work at this affective level, and high-frequency trading is regularly provided as a primary illustrative example of the contagious dynamics it produces. We challenge and complicate this account of the relation between consciousness, affect and media technologies (...) by drawing on the recent work of N. Katherine Hayles and by focusing in detail on the ways in which the ‘costs of consciousness’ are accounted for and negotiated in high-frequency trading. We suggest that traders actively develop modes of awareness accounting for the costs of consciousness, and that the necessary ‘stupidity’ of high-frequency trading algorithms as well as competition pose limits to the full automation of financial markets. (shrink)

The article discusses the role of intuition for insight. Creativity provides heuristic solutions to problems that are intractable if approached in standard, algorithmic ways. Intuition is claimed to occur during the incubation phase and to crucially depend on embodied memory and unconscious processing of memories, such as reconstruction and recreation. Two suggestions as to how memory contributes to intuition, and by which processes are analysed and compared: Barsalou & Prinz’ and Langley & Jones’.

[This manuscript is outdated; read chapter 7 of my book "Information, Intelligence and Idealism" instead, which is also available as full text on PhilPapers] Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which (...) imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Know-How of Musical PerformanceStephen DaviesMusicians make music; that is, the performance of music involves applied knowledge or know-how. Can we attain a discursive understanding of what the musician does, and does the attempt to achieve this put at risk the very art it aims to capture? In other words, what can be said of the nature of performance and does what we say turn a living practice into (...) a dead object? In this discussion, I aim to clarify the issues in terms of which these questions can be developed.Consider acquired practical knowledge or'knowing how.'1 It involves a learned skill or capacity to do something-typically, to perform an action-as in "I know how to ride a unicycle."2 'Knowing how'can be subdivided into types. A first variety is always conscious. For most people, mental arithmetic is of this kind. It is not mental arithmetic they are doing if they do not follow an appropriate rule, algorithm, procedure, or principle, even if they fluke the right answer. Many practical skills are of this sort. I know how to fill in tax forms, immigration documents, and hire purchase agreements. In all these cases, the working of the skill and the steps through which it is exercised are held inevitably before my mind. A second type of 'knowing how' might be termed "retrievable." I know how to drive a car. Most of the time, I am not aware of the processes, judgments, and procedures that are [End Page 154] involved. I do not have to think about my driving as I drive. Nevertheless, if I reflect on what I do, I can describe the steps or routines I go through. A third type of 'knowing how' is cognitively impenetrable. Cognitively impenetrable processes are opaque to introspection. Some bit of neurological hardware receives an input, processes it, and outputs some result, but the nature of that processing is not retrievable by consciousness. The skill is learned but the many computational, muscular, kinaesthetic, or other activities involved in the skill's application or execution are not available to the agent's awareness. From my own case, typical examples are of knowing how to walk, how to pick up a glass, and how to speak English.3 When a person intends to perform an action of this kind, he aims at it directly, as it were. He intends to turn his skateboard to the left, say, not to flex this muscle here and so. He can intend to produce a certain action as output, but the intermediate steps and movements involved in this, to the extent that they are controlled in an irretrievable fashion, are not what he can intend. Similarly, where 'knowing how' is retrievable but automatic, the action is intended directly. The intermediary steps and movements are not what are intended so long as the action remains automatic.In what ways is practical knowledge acquired? In some cases it can be learned from verbal descriptions of the appropriate basic actions and their sequences-"put the left leg in and shake it all about"-or of the relevant rules or algorithms-"multiply by π r2." In others, learning is by example, copying, or experimenting. Some procedures work better for some skills than others. Generally speaking, when we encourage children and stroke victims to walk we do not provide them with examples. By contrast, language skills are often conveyed via paradigm instances. And while it is possible to learn to drive a plane from an instruction manual alone, one is more likely to be successful by following and being assisted by an instructor. Always conscious skills are often acquired by memorizing the procedure or rule that must be kept in mind, whereas automatic but retrievable skills, because their application is unconscious, are usually acquired through physical repetition until the routine becomes unthinking. Nevertheless, even cognitively impenetrable skills might be learned by first following written instructions. What makes the skill impenetrable is not the method by which it is acquired but the inability to recall or describe the relevant steps, rules, or procedures when the skill is entrenched.4A given skill might be cognitively impenetrable for... (shrink)

Electroencephalogram has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy, wavelet packet energy, and wavelet packet entropy of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The (...) classification accuracy with Ei and WpEn is higher than MSpEn, which shows that Ei and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed. (shrink)

The article examines the dialogue between British cognitive science expert Murray Shanahan and the large language model Claude 3 Opus about “self-awareness” of artificial intelligence (AI). Adopting a text-centric approach, the author analyzes AI’s discourse through a hermeneutic lens from a reader’s perspective, irrespective of whether AI possesses consciousness or personhood. The article draws parallels between AI’s reasoning about the nature of consciousness and Buddhist concepts, especially the doctrine of dharmas, which underpins the Buddhist concept of anātman (“non-Self”). Basic classifications (...) of dharmas and their justification are examined in light of the Buddhist system of ideas about the foundations of an individual’s cognitive experience in the world. The author emphasizes that the problem of the Self as a linguistic and conceptual construct, rather than a real ontological category, was first formulated in the teachings of Buddha Shakyamuni who also proposed an “experimental” application of this concept in practices of systematic introspection (smṛti). The article contends that Claude’s discourse on self-awareness, even if it is just a tapestry of linguistic constructs woven by preset algorithms, could prove to be a source inspiring new approaches to the enigma of consciousness. This potential stems from its vast database, which is a melting pot of textual heritage from diverse human cultures. The author posits that examining AI-generated texts through the prism of Indian and Buddhist thought traditions can be eye-opening. Such an approach might help shed light on and overcome the unconscious cognitive biases and cultural blind spots within Western consciousness studies that have hindered their engagement with the full spectrum of human intellectual traditions. The author concludes that discovering different cultural sources in AI discourse and examining it from the perspective of various cultural traditions can: firstly, enrich the conceptual apparatus of cognitive studies; secondly, reveal universal cross-cultural patterns in understanding consciousness; thirdly, generate new research hypotheses and directions in studying not only artificial but also natural intelligence; fourthly, contribute to rethinking our understanding of the Other, by expanding the boundaries of what we today consider conscious or sentient. (shrink)

This article examines how Facebook’s Feed, its dynamic user interface, incorporates and refashions the capacity to temporalize cultural material and experience that has classically been attributed to subjectivity. I problematize the ambiguous historicity of digital culture across the experience of the ordinary that it produces by arranging the subjective time and ‘ruined’ bits of cultural material into algorithmic timelines. Drawing on recent media theory, I underscore the irreducible alienness of algorithmic temporalizations, which undermine habitual normalization. I show subjectivity (...) moves beyond identity and narrative closure through an unconscious affective investment in extremely popular algorithmic timelines. (shrink)

This piece, included in the drift special issue of continent. , was created as one step in a thread of inquiry. While each of the contributions to drift stand on their own, the project was an attempt to follow a line of theoretical inquiry as it passed through time and the postal service(s) from October 2012 until May 2013. This issue hosts two threads: between space & place and between intention & attention . The editors recommend that to experience the (...) drifiting thought that attention be paid to the contributions as they entered into conversation one after another. This particular piece is from the BETWEEN INTENTION & ATTENTION thread: Jeremy Fernando, Sitting in the Dock of the bay, watching... * R.H. Jackson, Reading Eyes * Gina Rae Foster, Nyctoleptic Nomadism: The Drift/Swerve of Knowing * Bronwyn Lay, Driftwood * Patricia Reed, Sentences on Drifitng * David Prater, drift: a way * * * * * 1. To drift is to be propelled by currents. 2. Sentences are textual currents. 3. To drift is to be com pelled with currents. 4. To be compelled is to be overcome by some force. 5. To be overcome by some force requires the instantiation of a weightless body (virtual or actual), upon which and through which a force can operate. 6. Forces can be physical, cognitive, unconscious and sensory. 7. Forces of drift have no particular telos; they incline bodies without knowable destination. 8. Drifting is nomadic, where nomadism includes movements between places, times (also time-zones), genres, thought and cognitive states. 9. Forces are attractors; attractivity is a force. 10. Attractivity is a relation between force and body. 11. Attractivity is relation of exteriority; it is of the domain of the foreign. 12. To drift is to become the prey of attraction, the prey of the foreign. 13. In the movement of becoming prey, one can either be eaten or feed into a re-composition of things. 14. Drifting is liminal; it can be equally productive and destructive. 15. The deviated course implied by drifting may yield novelty, yet may equally yield a perpetual deferral of any positionality. 16. The state of drifting is necessary to the creation of novelty. 17. The state of drifting can inhibit the creation of novelty. 18. The modern drift was celebrated as a form of emancipation, through the liberation of urban structures traced on foot, to the negation of banal tasks in riding on the open highway. In the attention economy, drifting has become a paradigm of labour, precarious and otherwise. 19. To drift in a novel fashion requires a necessary threshold between distraction and re-attraction. 20. Re-attraction is a form of going off-track, yet implies the fidelity to an alter force. Re-attraction is eccentric in inclination, that is, decentred, casting alter orbits of orientation. 21. Eccentricity is a form of drift defined as a body inflected by the force of heterogeneous inclination. Eccentricity is the fidelity to the becoming-foreign of the body as prey to attractivity. 22. Drift as eccentricity is equal to the creation of novel modes of inclination alter to the limits of normative orbits of determinate trajectories. 23. The effects of eccentric drifting can only be known post-movement. 24. All modes of drifting (natural and cognitive) are relative to some former position, although distances and their capacity for measurement are inconsistent. 25. Eccentric drifting does not result in relativism. Drifting is transitional, and, like a Moiré pattern, produces interferences between things. 26. Where there is drifting there are both side-effects and side-affects, which feed-back into the inclination of drift itself. 27. Side-effects and side-affects may produce conditions and sensibilities of failure. 28. The ethics of drifting play out in the responsiveness to side-effects/side-affects. 29. Eccentric drifting is not algorithmic, informational, strategic nor opportunistic; it is tactically lived experience. 30. Eccentric drifting is a dance with contingency, of repositioning a body (a body includes thought) through the affirmation of re-attraction. 31. Eccentric drifting is a form of love, a love of existence that proceeds strategic coupling with ideas, people, situations, places and so forth. 32. As a form of love, eccentric drifting can generate changes of state (and even changes of State). 33. Currents that propel/compel a state of eccentric drifting may be imaginary; a possible current includes the attractive force to that which is not (yet) present. 34. Eccentric drifting is not only a state of transitional movement, but most importantly includes the fabrication of imaginary forces that could propel/compel a given body and its spheres of interaction. 35. These sentences comment on drift, but are not exemplary of drifting. (shrink)

With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources (...) of dispute. The lens of this typology then allows us to look anew at the potential biases in insurance pricing implied by big data and machine learning, showing that despite utopic claims, social stereotypes continue to plague data, thus threaten to unconsciously reproduce these discriminations in insurance. To counter these effects, algorithmic fairness attempts to define mathematical indicators of non-bias. We argue that this may prove insufficient, since as it assumes the existence of specific protected groups, which could only be made visible through public debate and contestation. These are less likely if the right to explanation is realized through personalized algorithms, which could reinforce the individualized perception of the social that blocks rather than encourages collective mobilization. (shrink)

Both Bakhtin and Vygotsky, as we have seen, responded directly or indirectly to the challenge of Freud. Both attempted to account for their data without resorting to postulating an unconscious in the Freudian sense. By way of contrast, it is instructive here to recall Jacques Lacan—who, among others, has been a beneficiary of Bakhtin’s “semiotic reinterpretation” of Freud.17 Lacan’s case is intriguing, for he retains the unconscious while at the same time submitting Freudian psychoanalysis to rigorous criticism along (...) the lines of Bakhtin. By focusing attention on the dialogic word, he encourages a rereading of Freud in which the social element is crucial. As Lacan opens his essay “The Empty Word and the Full Word”:Whether it sees itself as an instrument of healing, of formation, or of exploration in depth, psychoanalysis has only a single intermediary: the patient’s Word….And every word calls for a reply.I shall show that there is no word without a reply, even if it meets no more than silence, provided that it has an auditor: this is the heart of its function in psychoanalysis.18The word is conceived as a tool not only in the external world but also of an autonomous internal world as well. And what emerges, it would seem, is a reinterpretation of the role of dialogue in the painful maturational process of the child. For Vygotsky, the child’s realization of his separateness from society is not a crisis; after all, his environment provides both the form and the content of his personality. From the start, dialogue reinforces the child’s grasp on reality, as evidenced by the predominantly social and extraverted nature of his earliest egocentric speech. For Lacan, on the contrary, dialogue seems to function as the alienating experience, the stade du miroir phase of a child’s development. The unconscious becomes the seat of all those problems that Bakhtin had externalized: the origin of personality, the possibilities of self-expression. The je-moi opposition in the mirror gives rise to that permanent for “a locus where there is constituted the je which speaks as well as he who has it speak.”19 And consequently, the Word takes on an entirely different coloration: it is no longer merely an ideological sign but a potent tool for repressing knowledge of that gap, the face in the mirror, the Other. Lacan’s celebrated inversion of Saussure’s algorithm, with the line between signifier and signified representing repression, created a powerful but ominous new role for language. The child is released from his alienating image only through discovering himself as Subject, which occurs with language; but this language will inevitably come to him from the Other. Thus speech is based on the idea of lack, and dialogue, on the idea of difference. 17. See Ivanov, “The Significance of M. M. Bakhtin’s Ideas,” p. 314.18. Jacques Lacan, “The Empty Word and the Full Word,” in Speech and Language in Psychoanalysis, ed. and trans. Anthony Wilden , p. 9.19. Lacan, from “La Chose freudienne” , quoted in Wilden, “Lacan and the Discourse of the Other,” in Speech and Language in Psychoanalysis, p. 266. Caryl Emerson, assistant professor of Russian literature at Cornell University, has translated The Dialogic Imagination, Problems of Dostoevsky’s Poetics . She is currently at work, on a study of Boris Godunov in Russian cultural history. (shrink)

The authors' aim is to build “more biologically plausible learning algorithms” that work in naturalistic environments. Given that, first, human learning and memory are inextricable, and, second, that much human learning is unconscious, can the authors' first research question of how people improve their learning abilities over time be answered without addressing these two issues? I argue that it cannot.

One of the most common contemporary approaches for developing an ethics of artificial intelligence (AI) involves elaborating guiding principles. This essay explores the limitations of this approach, using the history of bioethics as a comparative case. The examples of bioethics and recent AI ethics suggest that principles are difficult to implement in everyday practice, fail to direct individual action, and can frequently result in a pure proceduralism. The essay encourages an additional attention to virtue, which forms the dispositions of actors, (...) directs them to valued ends, and encourages right action in particular situation to complement principles. Practices and professions like medicine or warfighting that already have rich traditions of reflection on virtue ethics will be especially fruitful fields for developing an AI ethics of virtue. (shrink)

This essay introduces this special issue on virtue ethics in relation to military AI. It describes the current situation of military AI ethics as following that of AI ethics in general, caught between consequentialism and deontology. Virtue ethics serves as an alternative that can address some of the weaknesses of these dominant forms of ethics. The essay describes how the articles in the issue exemplify the value of virtue-related approaches for these questions, before ending with thoughts for further research.

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

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)

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

Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion (...) as a necessary condition for fairness. The second use involves treating criteria as sufficient conditions for fairness. Since the criteria are used for both roles, some researchers have treated them as both necessary and sufficient conditions, i.e., as definitions of algorithmic fairness. However, serious problems have been raised for the use of these fairness criteria. Under ordinary circumstances, it is impossible to satisfy multiple criteria at the same time. Moreover, there are counterexamples to both the sufficiency and necessity for fairness of each criterion. I argue that we should instead understand fairness criteria as merely providing evidence of fairness. In other words, satisfaction (or violation) of these criteria should be understood as potential evidence of fairness (or bias). Whether a criterion counts as evidence in a particular case will depend on stakeholders' background knowledge and the specific features of the system's task. This evidence account of fairness conditions provides guidance for recognizing both the appropriate uses and the limitations of fairness criteria. (shrink)

Historically, mental imagery has been defined as an experiential state - as something necessarily conscious. But most behavioural or neuroimaging experiments on mental imagery - including the most famous ones - don’t actually take the conscious experience of the subject into consideration. Further, recent research highlights that there are very few behavioural or neural differences between conscious and unconscious mental imagery. I argue that treating mental imagery as not necessarily conscious (as potentially unconscious) would bring much needed explanatory (...) unification to mental imagery research. It would also help us to reassess some of the recent aphantasia findings inasmuch as at least some subjects with aphantasia would be best described as having unconscious mental imagery. (shrink)

A best-seller in its French edition, the construction of this book is original and its success in the French market demonstrates its appeal. It is based on three principles: 1. An organization of the chapters by families of algorithms : exhaustive search, divide and conquer, etc. At the contrary, there is no chapter only devoted to a systematic exposure of, say, algorithms on strings. Some of these will be found in different chapters. 2. For each family of algorithms, an introduction (...) is given to the mathematical principles and the issues of a rigorous design, with one or two pedagogical examples. 3. For its most part, the book details 150 problems, spanning on seven families of algorithms. For each problem, a precise and progressive statement is given. More important, a complete solution is detailed, with respect to the design principles that have been presented ; often, some classical errors are pointed at. Roughly speaking, two thirds of the book are devoted to the detailed rational construction of the solutions. (shrink)

The algorithm, a building block of computer science, is defined from an intuitive and pragmatic point of view, through a methodological lens of philosophy rather than that of formal computation. The treatment extracts properties of abstraction, control, structure, finiteness, effective mechanism, and imperativity, and intentional aspects of goal and preconditions. The focus on the algorithm as a robust conceptual object obviates issues of correctness and minimality. Neither the articulation of an algorithm nor the dynamic process constitute the algorithm itself. Analysis (...) for implications in computer science and philosophy reveals unexpected results, new questions, and new perspectives on current questions, including the relationship between our informally construed algorithms and Turing machines. Exploration in terms of current computational and philosophical thinking invites further developments. (shrink)

For most of human history, human knowledge was considered to be something that was stored and captured by words. This began to change when Galileo said that the book of nature is written in the language of mathematics. Today, Dan Dennett and many others argue that all genuine scientific knowledge is in the form of mathematical algorithms. However, recently discovered neurocomputational algorithms can be used to justify the claim that there is genuine knowledge which is non-algorithmic. The fact that (...) these algorithms use prototype deployment, rather than mathematics or logic, gives us good reason to believe that there is a kind of knowledge that we derive from stories that is different from our knowledge of algorithms. Even though we would need algorithms to build a system that can make sense out of stories, we do not need to use algorithms when we ourselves embody a system that learns from stories. The success of the Galilean perspective in the physical sciences has often resulted in an attempt to mathematize the humanities. I am arguing that the dynamic neurocomputational perspective can give us a better understanding of how we get knowledge and wisdom from the stories told by disciplines such as Literature, History, Anthropology and Theology. This new neurological data can be used to justify the traditional pedagogy of these disciplines, which originally stressed the telling of stories rather than the learning of algorithms. (shrink)

One necessary condition on any adequate account of perception is clarity regarding whether unconscious perception exists. The issue is complicated, and the debate is growing in both philosophy and science. In this paper we consider the case for unconscious perception, offering three primary achievements. First, we offer a discussion of the underspecified notion of central coordinating agency, a notion that is critical for arguments that purportedly perceptual states are not attributable to the individual, and thus not genuinely perceptual. (...) We develop an explication of what it is for a representational state to be available to central coordinating agency for guidance of behavior. Second, drawing on this explication, we place a more careful understanding of the attributability of a state to the individual in the context of a range of empirical work on vision-for-action, saccades, and skilled typing. The results place pressure on the skeptic about unconscious perception. Third, reflecting upon broader philosophical themes running through debates about unconscious perception, we highlight how our discussion places pressure on the view that perception is a manifest kind, rather than a natural kind. In doing so, we resist the tempting complaint that the debate about unconscious perception is merely verbal. (shrink)

This comprehensive compendium provides a rigorous framework to tackle the daunting challenges of designing correct and efficient algorithms. It gives a uniform approach to the design, analysis, optimization, and verification of algorithms. The volume also provides essential tools to understand algorithms and their associated data structures. This useful reference text describes a way of thinking that eases the task of proving algorithm correctness. Working through a proof of correctness reveals an algorithm's subtleties in a way that a typical description does (...) not. Algorithm analysis is presented using careful definitions that make the analyses mathematically rigorous. (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)

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)

Traditionally, philosophers have appealed to the phenomenological similarity between visual experience and visual imagery to support the hypothesis that there is significant overlap between the perceptual and imaginative domains. The current evidence, however, is inconclusive: while evidence from transcranial brain stimulation seems to support this conclusion, neurophysiological evidence from brain lesion studies (e.g., from patients with brain lesions resulting in a loss of mental imagery but not a corresponding loss of perception and vice versa) indicates that there are functional and (...) anatomical dissociations between mental imagery and perception. Assuming that the mental imagery and perception do not overlap, at least, to the extent traditionally assumed, then the question arises as to what exactly mental imagery is and whether it parallels perception by proceeding via several functionally distinct mechanisms. In this review, we argue that even though there may not be a shared mechanism underlying vision for perception and conscious imagery, there is an overlap between the mechanisms underlying vision for action and unconscious visual imagery. On the basis of these findings, we propose a modification of Kosslyn’s model of imagery that accommodates unconscious imagination and explore possible explanations of the quasi-pictorial phenomenology of conscious visual imagery in light of the fact that its underlying neural substrates and mechanisms typically are distinct from those of visual experience. (shrink)

Digital platforms and application software have changed how people work in a range of industries. Empirical studies of the gig economy have raised concerns about new systems of algorithmic management exercised over workers and how these alter the structural conditions of their work. Drawing on the republican literature, we offer a theoretical account of algorithmic domination and a framework for understanding how it can be applied to ride hail and food delivery services in the on-demand economy. We argue (...) that certain algorithms can facilitate new relationships of domination by sustaining a socio-technical system in which the owners and managers of a company dominate workers. This analysis has implications for the growing use of algorithms throughout the gig economy and broader labor market. (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 information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Extended algorithmic logic (EAL) as introduced in [18] is a modified version of extended +-valued algorithmic logic. Only two-valued predicates and two-valued propositional variables occur in EAL. The role of the +-valued logic is restricted to construct control systems (stacks) of pushdown algorithms whereas their actions are described by means of the two-valued logic. Thus EAL formalizes a programming theory with recursive procedures but without the instruction CASE.The aim of this paper is to discuss EAL and prove the (...) completeness theorem. A complete formalization of EAL was announced in [20] but no proof of the completeness theorem was given. (shrink)

The possibilities of unconscious perception and unconscious bias prompt parallel debates about unconscious mental content. This chapter argues that claims within these debates alleging the existence of unconscious content are made fraught by ambiguity and confusion with respect to the two central concepts they involve: consciousness and content. Borrowing conceptual resources from the debate about unconscious perception, the chapter distills the two conceptual puzzles concerning each of these notions and establishes philosophical strategies for their resolution. (...) It then argues that empirical evidence for unconscious bias falls victim to these same puzzles, but that progress can be made by adopting similar philosophical strategies. Throughout, the chapter highlights paths forward in both debates, illustrates how they serve as fruitful domains in which to study the relationship between philosophy and empirical science, and uses their combined study to further understanding of a general theory of unconscious content. (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)

Recent findings in different areas of psychology and cognitive science have brought the unconscious mind back to center stage. However, the unconscious mind worry remains: What renders unconscious phenomena mental? I suggest a new strategy for answering this question, which rests on the idea that categorizing unconscious phenomena as “mental” should be scientifically useful relative to the explanatory research goals. I argue that this is the case if by categorizing an unconscious phenomenon as “mental” one (...) picks out explanatorily relevant similarities to a corresponding paradigmatically mental conscious phenomenon. Explanatory relevance is spelled out in terms of mechanistic norms. (shrink)

The concept of unconscious knowledge is fundamental for an understanding of human thought processes and mentation in general; however, the psychological community at large is not familiar with it. This paper offers a survey of the main psychological research currently being carried out into cognitive processes, and examines pathways that can be integrated into a discipline of unconscious knowledge. It shows that the field has already a defined history and discusses some of the features that all kinds of (...) unconscious knowledge seem to share at a deeper level. With the aim of promoting further research, we discuss the main challenges which the postulation of unconscious cognition faces within the psychological community. (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)

Having, in previous papers, distinguished at least three forms of consciousness , I now further examine their differences. This examination has some surprising results. Having argued that neither C1 nor C2 is a phenomenological state?and so different from CN?I now show that CN itself is best thought of as a subclass of a larger state . CS is the set of image?representation states. CN is that set of CS states that we are also C2 about. I argue that CN states (...) should be considered a subset of CS states because they share with other CS states imagistic representational powers as opposed to the sentential ones of C1 and C2. And I argue that there are good reasons to consider any state which utilises imagistic representation as a sensation. I then show that the notion of an unconscious sensation makes sense if we think of it as a CS state that is not C2. More radically, I conclude that either it makes sense to say that there are unfelt sensations or there are felt sensations we are not conscious about. While there are good reasons for saying one or the other of these surprising things, I argue that the second, more radical sounding, alternative is actually better motivated?and not quite so surprising as it sounds. (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)

Should decision-making algorithms be held to higher standards of transparency than human beings? The way we answer this question directly impacts what we demand from explainable algorithms, how we govern them via regulatory proposals, and how explainable algorithms may help resolve the social problems associated with decision making supported by artificial intelligence. Some argue that algorithms and humans should be held to the same standards of transparency and that a double standard of transparency is hardly justified. We give two arguments (...) to the contrary and specify two kinds of situations for which higher standards of transparency are required from algorithmic decisions as compared to humans. Our arguments have direct implications on the demands from explainable algorithms in decision-making contexts such as automated transportation. (shrink)