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.

This paper is a further reflection on my dialogue with Larry Hickman, director emeritus of the Center for Dewey Studies, and Daisaku Ikeda, president of the lay Buddhist organization Soka Gakkai International (sgi). One surprising outcome of this dialogue is how similar Deweyan pragmatism is to many forms of Mahayana Buddhism such as sgi. Here I survey some similarities between Hickman’s philosophy of technology and Buddhism by emphasizing value creation and criticism. (Soka Gakkai means value creating society.) I then explore (...) Peter D. Herschock’s, Buddhism and Intelligent Technology relying on Hickman to rectify Herschock’s philosophy of technology before discussing how Herschock’s insightful application of Buddhist ethics to ai and the internet accord with Hickman. (shrink)

The following paper offers a political and philosophical reading of ethically informed technological design practices to critically tackle the implicit regulative ideal in the design of social media as a means to digitally represent the liberal public sphere. The paper proposes that, when it comes to the case of social media platforms, understood along with the machine learning algorithms embedded in them as algorithmic technologies, ethically informed design has an implicit conception of democracy that parallels that of Jürgen (...) Habermas’ procedural democracy (Habermas, J. (1994). THREE NORMATIVE MODELS OF DEMOCRACY. Jurgen Habermas, 1(1).). That is, that democratic practices are encodable as procedures that produce valid discussion forums. Opposed to this, this paper suggests a turn to philosopher Jacques Rancière’s conception of politics as a guiding attitude towards technological design. This is done by, on the one side, using Rancière’s notions of “disagreement” and “distribution of the sensible” as the political starting point for the practice of design of algorithmic technologies. And, on the other, inspired by Karen Barad’s theories on agential realism (Barad, K. (2003). Posthumanist Performativity: Toward an Understanding of How Matter Comes to Matter. Signs: Journal of Women in Culture and Society, 28(3), 801–831. https://doi.org/10.1086/345321), by putting forward a political ontology of algorithmic technologies that reconceptualizes them in terms of how they assemble open-ended practices between human bodies and technological devices. (shrink)

This article analyzes local algorithmic practices resulting from the increased use of time-lapse (TL) imaging in fertility treatment. The data produced by TL technologies are expected to help professionals pick the best embryo for implantation. The emergence of TL has been characterized by promissory discourses of deeper embryo knowledge and expanded selection standardization, despite professionals having no conclusive evidence that TL improves pregnancy rates. Our research explores the use of TL tools in embryology labs. We pay special attention (...) to standardization efforts and knowledge-creation facilitated through TL and its incorporated algorithms. Using ethnographic data from five UK clinical sites, we argue that knowledge generated through TL is contingent upon complex human–machine interactions that produce local uncertainties. Thus, algorithms do not simply add medical knowledge. Rather, they rearrange professional practice and expertise. Firstly, we show how TL changes lab routines and training needs. Secondly, we show that the human input TL requires renders the algorithm itself an uncertain and situated practice. This, in turn, raises professional questions about the algorithm’s authority in embryo selection. The article demonstrates the embedded nature of algorithmic knowledge production, thus pointing to the need for STS scholarship to further explore the locality of algorithms and AI. (shrink)

Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the (...) bias itself, making it difficult to identify, mitigate, or evaluate using standard resources in epistemology and ethics. I demonstrate these points in the case of mitigation techniques by presenting what I call ‘the Proxy Problem’. One reason biases resist revision is that they rely on proxy attributes, seemingly innocuous attributes that correlate with socially-sensitive attributes, serving as proxies for the socially-sensitive attributes themselves. I argue that in both human and algorithmic domains, this problem presents a common dilemma for mitigation: attempts to discourage reliance on proxy attributes risk a tradeoff with judgement accuracy. This problem, I contend, admits of no purely algorithmic solution. (shrink)

The article brings to light the use of recommender systems as technologies of the self, complementing the observations in current literature regarding their employment as technologies of ‘soft’ power. User practices on the music recommendation website last.fm reveal that many users do not only utilize the website to receive guidance about music products but also to examine and transform an aspect of their self, i.e. their ‘music taste’. The capacity of assisting users in self-cultivation practices, however, is not (...) unique to last.fm but stem from certain properties shared by all recommendation systems. Furthermore, unlike other oft-studied digital/web technologies of the self which facilitate ‘self-publishing’ vis-à-vis virtual companions in social media, recommender algorithms themselves can act as ‘intimate experts’, accompanying users in their self-care practices. Thus, recommendation systems can facilitate both algorithmic control and creative self-transformation, which calls for a theorization of this new cultural medium as a space of tension. (shrink)

The world we inhabit is surrounded by ‘coded objects’ from credit cards to airplanes to telephones. Sadly the governance mechanisms of many of these technologies are only poorly understood, leading to the common premise that such technologies are ‘neutral’, thereby obscuring normative and power-related consequences of their design. In order to unpack supposedly neutral technologies, the following paper will try and foreground two of key questions around the technologies used on the global Internet: 1) how are (...) content regulatory regimes governed and 2) how are the algorithms embedded in software governed? The following paper will explore these two aspects in turn, before drawing conclusions on understanding the normative frameworks embedded in technological systems.Article first published online: 22 MARCH 2016. (shrink)

This article puts into perspective the practice of financial regulation in contemporary financial markets, while a new normative order has emerged. This order, heralded by algorithmic technologies, changes the conditions for the exercise of regulation: to date, it has not yet been fully acknowledged nor understood by regulatory bodies. Computer code, replacing speech and writing, induces a changeover from one normative order to another in contemporary markets: the norm, previously explicated with recourse to interpretation, is now replaced by (...) an order characterized by calculation. Such paradigm change is identified based on a discussion of works by Gilles Deleuze. In so doing, it contributes to the developing research programme in the cultures of high-frequency trading (Lange et al. 2016) and also points at broader issues for conceptualising financial regulation. (shrink)

The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion (Dietvorst et al. J Exp Psychol 144(1):114–126, 2015). In this paper, I confront this intuition and suggest that a more ‘extreme’ form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental (...) concepts of democracy and the Rule of Law. To provoke further consideration and illustrate that initial intuitions regarding democratic innovation may not always be accurate, I pose and explore four ways that AI and other forms of technology could be used to augment the representative democratic process. The augmentations range from voting online to the wholesale replacement of the legislature’s human representatives with algorithms. After first noting the intuition that less invasive forms of augmented democracy may be less objectionable than more extreme forms, I go on to critically assess whether the augmentation of existing systems satisfies or enhances ideas associated with democracy and the Rule of Law (provided by Dahl and Fuller). By imagining a (not too far-fetched) future in a (not too far-removed) democratic society, my conclusion is that, when it comes to democracy and the Rule of Law, intuitions regarding technology may lead us astray. (shrink)

The “impossibility results” in algorithmic fairness suggest that a predictive model cannot fully meet two common fairness criteria – sufficiency and separation – except under extraordinary circumstances. These findings have sparked a discussion on fairness in algorithms, prompting debates over whether predictive models can avoid unfair discrimination based on protected attributes, such as ethnicity or gender. As shown by Otto Sahlgren, however, the discussion of the impossibility results would gain from importing some of the tools developed in the philosophical (...) literature on feasibility. Utilizing these tools, Sahlgren sketches a cautiously optimistic view of how algorithmic fairness can be made feasible in restricted local decision-making. While we think it is a welcome move to inject the literature on feasibility into the debate on algorithmic fairness, Sahlgren says very little about what are the general gains of bringing in feasibility considerations in theorizing algorithmic fairness. How, more precisely, does it help us make assessments about fairness in algorithmic decision-making? This is what is addressed in this Reply. More specifically, our two-fold argument is that feasibility plays an important but limited role for algorithmic fairness. We end by offering a sketch of a framework, which may be useful for theorizing feasibility in algorithmic fairness. (shrink)

Algorithm is the core category and basic methods of the digital age, and advanced technologies such as big data, artificial intelligence, and blockchain all need to rely on various algorithm designs or take the algorithm as the underlying principle. However, due to the characteristics of algorithm design, application, and technology itself, there are also hidden worries such as algorithm black-box, algorithm discrimination, and difficulty in accountability in the operation process to varying degrees. This paper summarizes these problems into three (...) aspects: unexplainable, self-reinforcing and autonomous. Facing the opportunities and risks generated by the application of the algorithm in national governance, while actively promoting the development of algorithm technology to continuously promote the modernization process of national governance, it is also necessary to increase the governance of the algorithm. The practice has proved that enhancing the interpretability of algorithm, optimizing algorithm design, and adopting legal regulatory algorithm are the basic approaches to effective regulatory algorithm in the era of intelligent governance. (shrink)

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

The intelligent transportation system under the big data environment is the development direction of the future transportation system. It effectively integrates advanced information technology, data communication transmission technology, electronic sensing technology, control technology, and computer technology and applies them to the entire ground transportation management system to establish a real-time, accurate, and efficient comprehensive transportation management system that works on a large scale and in all directions. Intelligent video analysis is an important part of smart transportation. In order to improve (...) the accuracy and time efficiency of video retrieval schemes and recognition schemes, this article firstly proposes a segmentation and key frame extraction method for video behavior recognition, using a multi-time scale dual-stream network to extract video features, improving the efficiency and efficiency of video behavior detection. On this basis, an improved algorithm for vehicle detection based on Faster R-CNN is proposed, and the Faster R-CNN network feature extraction layer is improved by using the principle of residual network, and a hole convolution is added to the network to filter out the redundant features of high-resolution video images to improve the problem of vehicle missed detection in the original algorithm. The experimental results show that the key frame extraction technology combined with the optimized Faster R-CNN algorithm model greatly improves the accuracy of detection and reduces the leakage. The detection rate is satisfactory. (shrink)

The paper deals with the quandary of the neutrality and transparency of technologies. First, I show how this problem is connected with the image of the opening of 'black boxes' that is pivotal to much of science and technology studies. Second, methodological and socio-political dimensions of the 'black box' metaphor are discussed. Third, I analyze three typical solutions to the problem of the neutrality of technologies outside and inside constructivist technology studies. It is demonstrated that despite their apparent (...) differences, these solutions are similar in their logic of conceptualizing technology as a neutral intermediary. Forth, I look for an alternative to this logic in the actor-network theory of Bruno Latour. Here technologies are conceived in terms of an eventful association of heterogeneous entities irreducible to its conditions of possibility. The construction of technologies is understood as mediation, or as a 'making-do' process where creators are surprised by their creations and vice versa. In Latour's actor-network, technologies are interpreted as opaque and non-neutral entities. Finally, I turn to some object-lessons from smart technologies powered by neural networks to demonstrate that these are empirical vindications of Latour's conception of technical mediation. Particular attention is paid to the opacity and (non)interpretability of machine learning algorithms. (shrink)

This book focuses on how new technologies are raising and reshaping ethical questions and practices which aim to automate ethics into program outputs. With new powerful technologies come enhanced capacities to act, which in turn require new ethical concepts for guiding just and fair actions in the use of these new capabilities. The new algorithmic regimes, for their ethical articulation, build on prior ethics discourses in computer and information ethics, as well as the philosophical traditions of ethics (...) generally. Especially as our technologies become more autonomous, operating alongside us in the home, workplace or on the roads, ethics has the potential to limit negative effects and shape the new technical terrains in a more humanly recognizable way. The volume covers a critique of human-centered AI, the effects of AI and the internet of things in the domain of human resource management, how decentralized finance applications on the blockchain encode ethical norms into 'smart contracts,' and the personal surveillance risks of audio beacon technology operating invisibly in our cellphones. Scholars and students from many backgrounds, as well as policy makers, journalists and the general reading public will find a multidisciplinary approach to questions posed by research in algorithmic ethics from the fields of Management, Sociology, Social Policy, Public Service, Religion and Interactive Media. (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)

Big Data evangelists often argue that algorithms make decision-making more informed and objective—a promise hotly contested by critics of these technologies. Yet, to date, most of the debate has focused on the instruments themselves, rather than on how they are used. This article addresses this lack by examining the actual practices surrounding algorithmic technologies. Specifically, drawing on multi-sited ethnographic data, I compare how algorithms are used and interpreted in two institutional contexts with markedly different characteristics: web journalism (...) and criminal justice. I find that there are surprising similarities in how web journalists and legal professionals use algorithms in their work. In both cases, I document a gap between the intended and actual effects of algorithms—a process I analyze as “decoupling.” Second, I identify a gamut of buffering strategies used by both web journalists and legal professionals to minimize the impact of algorithms in their daily work. Those include foot-dragging, gaming, and open critique. Of course, these similarities do not exhaust the differences between the two cases, which are explored in the discussion section. I conclude with a call for further ethnographic work on algorithms in practice as an important empirical check against the dominant rhetoric of algorithmic power. (shrink)

Dehumanization by algorithms raises important issues for business and society. Yet, these issues remain poorly understood due to the fragmented nature of the evolving dehumanization literature across disciplines, originating from colonialism, industrialization, post-colonialism studies, contemporary ethics, and technology studies. This article systematically reviews the literature on algorithms and dehumanization (n = 180 articles) and maps existing knowledge across several clusters that reveal its underlying characteristics. Based on the review, we find that algorithmic dehumanization is particularly problematic for human resource (...) management and the future of work, managerial decision-making, consumer ethics, hard- and soft-law regulation, and basic values, including privacy and consumer rights. From the literature synthesis, we also derive the following definition of algorithmic dehumanization: the act of using algorithms and data in a way that results in the intentional or unintentional treatment of individuals and/or groups as less than fully human, thereby violating human rights, including liberty, equality, and dignity. Ultimately, we present a dehumanization avoidance model that serves as a structured research agenda and practical guide to address the challenges raised by algorithmic dehumanization. Thus, the model indicates promising pathways for future research at the intersection of AI and society and facilitates reflection on organizational processes that support improving corporate capabilities and managerial decision-making to avoid algorithmic dehumanization. (shrink)

With increasing use of automated algorithmic decision-making, issues of algorithmic fairness have attracted much attention lately. In this growing literature, existing concepts from ethics and political philosophy are often applied to new contexts. The reverse—that novel insights from the algorithmic fairness literature are fed back into ethics and political philosophy—is far less established. However, this short commentary on Baumann and Loi (Philosophy & Technology, 36(3), 45 2023) aims to do precisely this. Baumann and Loi argue that among (...) algorithmic group fairness measures proposed, one—sufficiency (well-calibration) is morally defensible for insurers to use, whereas independence (statistical parity or demographic parity) and separation (equalized odds) are not normatively appropriate in the insurance context. Such a result may seem to be of relatively narrow interest to insurers and insurance scholars only. We argue, however, that arguments such as that offered by Baumann and Loi have an important but so far overlooked connection to the derivation of the minimal state offered by Nozick (1974) and thus to political philosophy at large. (shrink)

There is something frustrating in the concept of algorithm that can even worry: its limitations. An algorithm does not need the time to define itself, it possesses in its structure everything that defines it and can work exclusively in a site, such as a computer, which is itself another finite system. Whenever a particular algorithm will be executed it will always be inexorably equal to itself because the number of possible states is finite. A logical­mathematical algorithm is essentially very different (...) from a living being that reveals its being only in the passage of time, in the relationship with the environment, in the happening of events. On the contrary there is a strong relationship between living things and the art, since art essentially needs time to express itself. The art has a time dimension, if for no other reason, because the purpose of art is reaching human beings. But art, through technology, can also broaden its horizons. Art can make use of the technology and can do exactly as the man whose expression is, to grow, to expand, to discover new identities, to conduct an investigation in those distant places, on the border with the irrational. (shrink)

This essay addresses a version of Jerome Frank’s question – ‘Are Judges Human?’ – asking instead: are human judges necessary? It begins, in section II, by outlining the technological developments which inform the view that they are not and critically evaluates the juristic position that seemingly endorses it. That position is labelled ‘technological evangelism’ and it consists of three claims about law and adjudication: the certainty, determinacy and partiality claims. Section III shows that these three claims are utterly incompatible with (...) what it calls standard and non-standard views of adjudication and law, while section IV considers some ways in which proponents of technological evangelism might try to reject standard and non-standard views. That section concludes that no plausible efforts have so far been made by technological evangelists to reject standard and non-standard views, and that those views therefore maintain their existing explanatory and normative priority. The overall conclusion of the essay is that technological evangelism is not a critical explanatory and normative engagement with law and adjudication as we know them, but an effort to replace them: not a game-changing intervention, but a game-ending one. (shrink)

As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such as Facebook, YouTube and Twitter. This article provides an accessible technical primer on how algorithmic moderation works; (...) examines some of the existing automated tools used by major platforms to handle copyright infringement, terrorism and toxic speech; and identifies key political and ethical issues for these systems as the reliance on them grows. Recent events suggest that algorithmic moderation has become necessary to manage growing public expectations for increased platform responsibility, safety and security on the global stage; however, as we demonstrate, these systems remain opaque, unaccountable and poorly understood. Despite the potential promise of algorithms or ‘AI’, we show that even ‘well optimized’ moderation systems could exacerbate, rather than relieve, many existing problems with content policy as enacted by platforms for three main reasons: automated moderation threatens to further increase opacity, making a famously non-transparent set of practices even more difficult to understand or audit, further complicate outstanding issues of fairness and justice in large-scale sociotechnical systems and re-obscure the fundamentally political nature of speech decisions being executed at scale. (shrink)

Scholars of critical algorithmic studies, including those from geography, anthropology, Science and Technology Studies and communication studies, have begun to consider how algorithmic devices and platforms facilitate democratic practices. In this article, I draw on a comparative ethnography of two alternative open-source algorithmic platforms – Decide Madrid and vTaiwan – to consider how they are dynamically constituted by differing algorithmic–human relationships. I compare how different algorithmic–human relationships empower citizens to influence political decision-making through proposing, commenting, (...) and voting on the urban issues that should receive political resources in Taipei and Madrid. I argue that algorithmic empowerment is an emerging process in which algorithmic–human relationships orient away from limitations and towards conditions of plurality, actionality, and power decentralisation. This argument frames algorithmic empowerment as bringing about empowering conditions that allow (underrepresented) individuals to shape policy-making and consider plural perspectives for political change and action, not as an outcome-driven, binary assessment (i.e. yes/no). This article contributes a novel, situated, and comparative conceptualisation of algorithmic empowerment that moves beyond technological determinism and universalism. (shrink)

This article aims to make a distinction among contemporary artworks that use algorithmic technologies. Departing from Science, Technology and Society (STS) studies, the focus is given to artworks that use algorithmic agencies, as assemblages of human and nonhuman entities. Making an ethno-methodological analysis of three artworks, The Pitiful Story of Deniz Yılmaz (2015–present) of Bager Akbay, Artificial Intelligence for Governance, the Kitty AI (2016) of Pınar Yoldaş and Plantoid (2015) of Primavera Di Filippi, the article examines paratactical (...) use of algorithmic agencies in contemporary artistic practice. Exploring, revealing and learning from the tactics used by platforms, corporations and institutions that control and govern, paratactical use of algorithmic agencies generates a self-reflexive perspective on such tactics of control. Paratactical works also explore absurdities and contingencies embedded in algorithmic technologies. They work within failures and errors, and perform subversive ways of thinking, acting and enacting. In conclusion, paratactical works deal with the reproduction, distribution and normalization of ignorance, extinction, degeneration, corruption and destruction. (shrink)

After reviewing various informal definitions, the article shows how algorithms’ implementation still presents clear limitations, despite Big Tech’s enthusiastic narratives. We then show the ideological nature of the concept of algorithmic objectivity, developing a practical account of ideology. We argue that, along with other devices, algorithmic objectivity imbues subjects with a structural reflexivity deficit, by virtue of which Big Tech’s power practices are not perceived as such. In its conclusion, the article argues for a critical ethics for the (...) digital age, capable of taking into account the power dynamics that run through it. (shrink)

Algorithms, or rather algorithmic actions, are seen as problematic because they are inscrutable, automatic, and subsumed in the flow of daily practices. Yet, they are also seen to be playing an important role in organizing opportunities, enacting certain categories, and doing what David Lyon calls “social sorting.” Thus, there is a general concern that this increasingly prevalent mode of ordering and organizing should be governed more explicitly. Some have argued for more transparency and openness, others have argued for more (...) democratic or value-centered design of such actors. In this article, we argue that governing practices—of, and through algorithmic actors—are best understood in terms of what Foucault calls governmentality. Governmentality allows us to consider the performative nature of these governing practices. They allow us to show how practice becomes problematized, how calculative practices are enacted as technologies of governance, how such calculative practices produce domains of knowledge and expertise, and finally, how such domains of knowledge become internalized in order to enact self-governing subjects. In other words, it allows us to show the mutually constitutive nature of problems, domains of knowledge, and subjectivities enacted through governing practices. In order to demonstrate this, we present attempts to govern academic writing with a specific focus on the algorithmic action of Turnitin. (shrink)

Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker, and measured perceived fairness, trust, and emotional response. With the (...) mechanical tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies. (shrink)

An AI-based “patient preference predictor” (PPP) is a proposed method for guiding healthcare decisions for patients who lack decision-making capacity. The proposal is to use correlations between sociodemographic data and known healthcare preferences to construct a model that predicts the unknown preferences of a particular patient. In this paper, I highlight a distinction that has been largely overlooked so far in debates about the PPP–that between algorithmic prediction and decision-making–and argue that much of the recent philosophical disagreement stems from (...) this oversight. I show how three prominent objections to the PPP only challenge its use as the sole determinant of a choice, and actually support its use as a source of evidence about patient preferences to inform human decision-making. The upshot is that we should adopt the evidential conception of the PPP and shift our evaluation of this technology towards the ethics of algorithmic prediction, rather than decision-making. (shrink)

This article examines how algorithms mediate the human faculty of judgment within the context of social media. Challenging the common view that algorithms ‘undermine’ or ‘eliminate’ human judgment, I argue instead that they mediate the human-world relations in which judgments emerge. Drawing on Hannah Arendt’s phenomenological approach to identity and judgment, the article deconstructs prevailing assumptions about ‘human judgment’ and ‘algorithmic judgment’ and proposes a different approach to understand user-algorithm relations as co-constitutive rather than oppositional. In line with the (...) ‘empirical turn’ in the philosophy of technology, which emphasizes the importance of grounding philosophical inquiry in lived experience, the proposed philosophical approach is developed and illustrated through a case study of ADHD-related content on TikTok. The case study demonstrates how algorithmic amplification becomes part of self-apprehension and public discourse. Through this lens, the article explores the interplay between self-disclosure, judgment, and the collective formation of political subjectivity, revealing the productive and disruptive roles algorithms play in these processes. (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)

Much discussion of the ethics of algorithms has focused on harms to autonomy—especially harms stemming from manipulation. Nonetheless, although manipulation can often be harmful, we suggest that in certain contexts it may not impair autonomy. To fully assess the impact of algorithm design on autonomy, we argue for a need to move beyond a focus on manipulation towards a multidimensional account of autonomy itself. Drawing on the autonomy literature and recent data ethics, we propose a novel account which takes autonomy (...) to supervene on three distinct but related elements: agency, authenticity and individual control over decision-making. By distinguishing autonomy from control, the account can explain the variable effects of manipulation on user autonomy within algorithm-driven systems. In particular, it can explain why improving user control may improve autonomy in some contexts, while in other contexts—such as in some kinds of newsfeeds—certain algorithm designs that instead _reduce_ user control may nevertheless improve autonomy. As a result, the account can accommodate the sometimes convoluted interplay between control, autonomy, manipulation, and commercial versus prosocial design goals. (shrink)

The now well-known impossibility results of algorithmic fairness demonstrate that an error-prone predictive model cannot simultaneously satisfy two plausible conditions for group fairness apart from exceptional circumstances where groups exhibit equal base rates. The results sparked, and continue to shape, lively debates surrounding algorithmic fairness conditions and the very possibility of building fair predictive models. This article, first, highlights three underlying points of disagreement in these debates, which have led to diverging assessments of the feasibility of fairness in (...) prediction-based decision-making. Second, the article explores whether and in what sense fairness as defined by the conjunction of the implicated fairness conditions is (un)attainable. Drawing on philosophical literature on the concept of feasibility and the role of feasibility in normative theory, I outline a cautiously optimistic argument for the _diachronic feasibility_ of fairness. In line with recent works on the topic, I argue that fairness can be _made possible_ through collective efforts to eliminate inequalities that feed into local decision-making procedures. (shrink)

When agents insert technological systems into their decision-making processes, they can obscure moral responsibility for the results. This can give rise to a distinct moral wrong, which we call “agency laundering.” At root, agency laundering involves obfuscating one’s moral responsibility by enlisting a technology or process to take some action and letting it forestall others from demanding an account for bad outcomes that result. We argue that the concept of agency laundering helps in understanding important moral problems in a number (...) of recent cases involving automated, or algorithmic, decision-systems. We apply our conception of agency laundering to a series of examples, including Facebook’s automated advertising suggestions, Uber’s driver interfaces, algorithmic evaluation of K-12 teachers, and risk assessment in criminal sentencing. We distinguish agency laundering from several other critiques of information technology, including the so-called “responsibility gap,” “bias laundering,” and masking. (shrink)

The article explores the way in which new objects of study, such as dance performance and art–practice, emerge in algorithmic aesthetics. The advent of digital communicative practices shapes new means of formalization of dance performance scripts, which nowadays have various technological solutions, including algorithmic ones. Classical aesthetics is unable to describe the technological modernization of artistic expression. The author offers a general framework of algorithmic aesthetics to study of dance performance and art practice.

The article discusses the use of algorithmic models in finance (algo or high frequency trading). Algo trading is widespread but also somewhat controversial in modern financial markets. It is a form of automated trading technology, which critics claim can, among other things, lead to market manipulation. Drawing on three cases, this article shows that manipulation also can happen in the reverse way, meaning that human traders attempt to make algorithms ‘make mistakes’ by ‘misleading’ them. These attempts to manipulate are (...) very simple and immediately transparent to humans. Nevertheless, financial regulators increasingly penalize such attempts to manipulate algos. The article explains this as an institutionalization of algo trading, a trading practice which is vulnerable enough to need regulatory protection. (shrink)

Algorithms: Design and Analysis is a textbook designed for undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. The book offers adequate mix of both theoretical and mathematical treatment of the concepts. It covers the basics, design techniques, advanced topics and applications of algorithms. The book will also serve as a useful reference for researchers and practising programmers whointend to pursue a career in algorithm designing. The book is also indented for students preparing for campus interviews (...) and competitive examinations. (shrink)

With the development and application of information technology, the digitization of information management and the virtualization of physical models have become very important technical application fields in the world. The establishment of the 3D landscape model and the realization of the 3D geographic information system are based on this, and there is not only a wide range of development prospects in many aspects such as urban planning and management, planning and design, local government construction, housing industry development, land monitoring and (...) management, and environmental monitoring and evaluation. There are not only research studies and formulations but also a practical significance in other analysis, evaluation, decision making, and other departments. In recent years, the research of 3D city modeling has been developed rapidly, and most of the existing 2D-GIS can be transformed into 3D visual landscape. Based on the three-dimensional geographic information system and desktop drawing software AutoCAD, this paper takes the two-dimensional line drawing of a residential district as the base map, expounds the design process of the three-dimensional simulation model of the urban community from the aspects of map preprocessing, building simulation model construction, texture mapping, and virtual visualization, and discusses the problems encountered in it. The main contributions of this paper are that this technology puts forward a new solution for the integrated corridor operation and maintenance management, greatly improves the intelligent management level of the integrated corridor operation and maintenance work, and simplifies the complex integrated corridor operation and maintenance management. It is helpful to improve the work efficiency of operation and maintenance, reduce the dependence of personnel, and respond to the long-term construction needs of the smart city at the same time. (shrink)

The paper deals with the difference between who and what we are in order to take an ethical perspective on algorithms and their regulation. The present casting of ourselves as homo digitalis implies the possibility of projecting who we are as social beings sharing a world, into the digital medium, thereby engendering what can be called digital whoness, or a digital reification of ourselves. A main ethical challenge for the evolving digital age consists in unveiling this ethical difference, particularly when (...) dealing with algorithms and their regulation in the context of human relationships. The paper addresses by way of example some issues raised by autonomous cars. (shrink)

This paper is about the opposite of judgement and calculation. This opposition has been a traditional anchor of critiques concerned with the rise of AI decision making over human judgement. Contrary to these approaches, it is argued that human judgement is not and cannot be replaced by calculation, but that it is human judgement that contextualises computational structures and gives them meaning and purpose. The article focuses on the epistemic structure of algorithms and artificial neural networks to find that they (...) always depend on human judgement to be related to real life objects or purposes. By introducing the philosophical concept of judgement, it becomes clear that the property of judgement to provide meaning and purposiveness is based on the temporality of human life and the ambiguity of language, which quantitative processes lack. A juxtaposition shows that calculations and clustering can be used and referred to in more or less prejudiced and reflecting as well as opaque and transparent ways, but thereby always depend on human judgement. The paper clearly asserts that the transparency of AI is necessary for their autonomous use. This transparency requires the explicitness of the judgements that constitute these computational structures, thereby creating an awareness of the conditionality of such epistemic entities. (shrink)

In a 1955 lecture the physicist Richard Feynman reflected on the place of doubt within scientific practice. ‘Permit us to question, to doubt, to not be sure’, proposed Feynman, ‘it is possible to live and not to know’. In our contemporary world, the science of machine learning algorithms appears to transform the relations between science, knowledge and doubt, to make even the most doubtful event amenable to action. What might it mean to ‘leave room for doubt’ or ‘to live and (...) not to know’ in our contemporary culture, where the algorithm plays a major role in the calculability of doubts? I propose a posthuman mode of doubt that decentres the liberal humanist subject. In the science of machine learning algorithms the doubts of human and technological beings nonetheless dwell together, opening onto a future that is never fully reduced to the single output signal, to the optimised target. (shrink)

Technology is developing fast - so fast that it threatens to overwhelm the very species whose genius lies in its technological cunning: us. From the metaverse to genetic engineering and mood-altering pharmaceuticals, to cybersex and cyberwar and the widespread automation of work, new technologies are rewriting the terms of our existence, not in a neutral spirit of 'progress' but in line with the priorities of power and profit, and in ways that often work against the grain of our fundamental (...) being.In this timely, provocative book, Richard King argues that we need to evolve a more critical attitude to new technologies if we are to avoid a world in which humans are no different in kind from algorithmic machines. The stakes could not be higher. As science, technology and capitalism fuse into a single system, and activists and entrepreneurs talk of a 'post-human' future in which individuals will transform themselves using powerful computers and biotechnologies, we are entering unchartered territory - a territory marked with the mapmaker's warning, Here Be Dragons... Here Be Monsters. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using (...) these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

The algorithms and processes of modern technologies affect almost all aspects of our lives. No human being is making individual subjective choices in each, or any, instance of these processes—all cases are treated with perfect technological indifference. Hence, a commonsense assumption about the technologies we interact with, and the algorithms many of them implement, is that they are neutral and impartial. Of course, this is simply not the case. As has been extensively documented and discussed, bias exists in (...) all facets of technology, from how and why it was first conceived and then developed to its inputs, processes and outputs. I examine instances of technological bias in visual representation and criminal justice technologies, and argue that they produce, among other epistemic concerns, a form of hermeneutical injustice by means of obstructing the deployment of the necessary and otherwise available hermeneutical resources needed to accurately understand these technologies and their effects. Such epistemic harm is inevitable, unless the relationship between our human biases and the technological processes produced by us is made explicit and significantly rethought. (shrink)

The tendency towards an increasing integration of the informational web into our daily physical world (in particular in so-called Ambient Intelligent technologies which combine ideas derived from the field of Ubiquitous Computing, Intelligent User Interfaces and Ubiquitous Communication) is likely to make the development of successful profiling and personalization algorithms, like the ones currently used by internet companies such as Amazon, even more important than it is today. I argue that the way in which we experience ourselves necessarily goes (...) through a moment of technical mediation. Because of this algorithmic profiling that thrives on continuous reconfiguration of identification should not be understood as a supplementary process which maps a pre-established identity that exists independently from the profiling practice. In order to clarify how the experience of one’s identity can become affected by such machine-profiling a theoretical exploration of identity is made (including Agamben’s understanding of an apparatus, Ricoeur’s distinction between idem- and ipse-identity, and Stiegler’s notion of a conjunctive–disjunctive relationship towards retentional apparatuses). Although it is clear that no specific predictions about the impact of Ambient Intelligent technologies can be made without taking more particulars into account, the theoretical concepts are used to describe three general scenarios about the way wherein the experience of identity might become affected. To conclude, I argue that the experience of one’s identity may affect whether the cases of unwarranted discrimination resulting from ubiquitous differentiations and identifications within an Ambient Intelligent environment, will become a matter of societal concern. (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)

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

The article examines the impact of contemporary intellectual technologies on human subjectivity through the lens of 20th-century philosophical reflection. It explores the transformation of the relationship between humans and technology in a context where technological systems transcend the traditional understanding of technology as merely an extension of human capabilities. Drawing on the conceptual framework of the philosophy of technology (M. Heidegger, J. Ortega y Gasset, J. Ellul, H. Marcuse), the author identifies three key aspects of this transformation. First, the (...) article considers the process by which various facets of human activity – cognitive processes, emotional reactions, social relationships, and creativity – are transformed into “standing-reserve” (Bestand) for technological systems. Second, it analyzes the phenomenon of the erosion of practices that reproduce and develop human experience, as evidenced by the standardization of cognitive processes, the emergence of intellectual dependency, and cultural homogenization. Third, it investigates the problem of technological determinism, which, in the context of intellectual technologies, takes on the character of not merely an external constraint but an active construction of human subjectivity, agency. Special attention is given to the mechanisms through which intellectual technologies transform processes of identity formation, decision-making, and social interaction. Furthermore, the article considers the intersubjective interaction between humans and intellectual technology, emphasizing that an imbalance in this relationship may lead to the erosion of human subjectivity. In closing, the article advocates for the development of new approaches to the legal regulation of intellectual technologies to preserve the balance between technological advancement and the maintenance of human autonomy. The author concludes that the relationship between humans and intellectual technologies has become a central issue in contemporary philosophical anthropology: preserving human subjectivity in the era of artificial intelligence will require a critical rethinking and partial transformation of traditional conceptions of human nature, values, and the normative foundations of human activity. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves (“gaming the system” in particular), the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected (...) since sophisticated algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

Institutions are increasingly employing algorithms to provide performance feedback to individuals by tracking productivity, conducting performance appraisals, and developing improvement plans, compared to traditional human managers. However, this shift has provoked considerable debate over the effectiveness and fairness of algorithmic feedback. This study investigates the effects of negative performance feedback (NPF) on the attitudes, cognition and behavior of medical researchers, comparing NPF from algorithms versus humans. Two scenario-based experimental studies were conducted with a total sample of 660 medical researchers (...) (algorithm group: N1 = 411; human group: N2 = 249). Study 1 analyzes the differences in scientific misconduct, moral disengagement, and algorithmic attitudes between the two sources of NPF. The findings reveal that NPF from algorithms shows higher levels of moral disengagement, scientific misconduct, and negative attitudes towards algorithms compared to NPF from humans. Study 2, grounded in trait activation theory, investigates how NPF from algorithms triggers individual’s egoism and algorithm aversion, potentially leading to moral disengagement and scientific misconduct. Results indicate that algorithm aversion triggers individuals’ egoism, and their interaction enhances moral disengagement, which in turn leads to increased scientific misconduct among researchers. This relationship is also moderated by algorithmic transparency. The study concludes that while algorithms can streamline performance evaluations, they pose significant risks to scientific misconduct of researchers if not properly designed. These findings extend our understanding of NPF by highlighting the emotional and cognitive challenges algorithms face in decision-making processes, while also underscoring the importance of balancing technological efficiency with moral considerations to promote a healthy research environment. Moreover, managerial implications include integrating human oversight in algorithmic NPF processes and enhancing transparency and fairness to mitigate negative impacts on medical researchers’ attitudes and behaviors. (shrink)