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Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI’s impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter this trend by exploring the reverse perspective: how do cities affect the development, and expose the present limits, of SDCs? The contribution of this paper is threefold. First, by comparing urban and nonurban environments and thoroughly examining the relationship between computer vision and city-specific sociality and form, it defines machine autonomy/automation as a function of the sociotechnical milieu in which an AI system operates. Second, and related, the paper problematizes the notion of SDCs as autonomous technologies and the role it plays in envisioning contending policy arrangements and technical solutions for achieving full driving automation. Finally, the article offers insight into a materialist and spatialized understanding of AI—namely, not as an abstract quality susceptible to replication within discrete machines, but rather as a distributed property emerging through embodied interactions among a multiplicity of agents (human, non-human, and technological) within/with their environments.

Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common. Despite the often-discussed biases in the algorithms' training and unequally borne benefits, almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these practices that enables semantic knowledge or analysis within urban contexts, especially within the context of using and occupying urban space. This paper will critique existing uses of artificial intelligence and computer vision in urban practices to propose a new framework for understanding people, action, and public space. This paper revisits Geertz's use of thick descriptions in generating interpretive theories of culture and activity and uses this lens to establish a framework to evaluate the varied uses of computer vision technologies that weigh meaning. We discuss how the framework's positioning may differ (and conflict) between different users of the technology. This paper also discusses the current use and training of deep learning algorithms and how this process limits semantic learning and proposes three potential methodologies for gaining a more contextually specific, urban-semantic, description of urban space relevant to urbanists. This paper contributes to the critical conversations regarding the proliferation of artificial intelligence by challenging the current applications of these technologies in the urban environment by highlighting their failures within this context while also proposing an evolution of these algorithms that may ultimately make them sensitive and useful within this spatial and cultural milieu., Comment: 21 pages, 3 figures

Smart city discourses often invoke the Panopticon, a disciplinary architecture designed by Jeremy Bentham and popularly theorized by Michel Foucault, as a model for understanding the social impact of AI technologies. This framing focuses attention almost exclusively on the negative ramifications of Urban AI, correlating ubiquitous surveillance, centralization, and data consolidation with AI development, and positioning technologies themselves as the driving factor shaping privacy, sociality, equity, access, and autonomy in the city. This paper describes an alternative diagram for Urban AI—the Polyopticon: a distributed, polyvalent, multi-modal network of synthetic intelligences. It posits that fourth industrial revolution technologies change the political, social, and psychodynamic relationships of sentience and witness in the city, shifting the effects of watching and watched beyond the exclusive domain of top-down surveillance and discipline. The Polyopticon poses a more expansive and ambivalent spectrum of possibilities for Urban AI scenarios, one that undermines the totalizing, singular, and cerebral notion of intelligence that so often characterizes Urban AI and smart city critiques.

Deep learning developers typically rely on deep learning software frameworks (DLSFs)—simply described as pre-packaged libraries of programming components that provide high-level access to deep learning functionality. New DLSFs progressively encapsulate mathematical, statistical and computational complexity. Such higher levels of abstraction subsequently make it easier for deep learning methodology to spread through mimesis (i.e., imitation of models perceived as successful). In this study, we quantify this increase in abstraction and discuss its implications. Analyzing publicly available code from Github, we found that the introduction of DLSFs correlates both with significant increases in the number of deep learning projects and substantial reductions in the number of lines of code used. We subsequently discuss and argue the importance of abstraction in deep learning with respect to ephemeralization, technological advancement, democratization, adopting timely levels of abstraction, the emergence of mimetic deadlocks, issues related to the use of black box methods including privacy and fairness, and the concentration of technological power. Finally, we also discuss abstraction as a symptom of an ongoing technological metatransition.

This article provides a course of correction in the discourse surrounding human-centric AI by elucidating the philosophical underpinning that serves to create a view that AI is divorced from human-centric values. Next, we espouse the need to explicitly designate stakeholder- or community-centric values which are needed to resolve the issue of alignment. To achieve this, we present two frameworks, Ubuntu and maximum feasible participation. Finally, we demonstrate how employing the aforementioned frameworks in AI can benefit society by flattening the current top-down social hierarchies as AI is currently being utilized. Implications are discussed.

Artificial intelligence (AI) is increasingly relied upon by clinicians for making diagnostic and treatment decisions, playing an important role in imaging, diagnosis, risk analysis, lifestyle monitoring, and health information management. While research has identified biases in healthcare AI systems and proposed technical solutions to address these, we argue that effective solutions require human engagement. Furthermore, there is a lack of research on how to motivate the adoption of these solutions and promote investment in designing AI systems that align with values such as transparency and fairness from the outset. Drawing on insights from psychological theories, we assert the need to understand the values that underlie decisions made by individuals involved in creating and deploying AI systems. We describe how this understanding can be leveraged to increase engagement with de-biasing and fairness-enhancing practices within the AI healthcare industry, ultimately leading to sustained behavioral change via autonomy-supportive communication strategies rooted in motivational and social psychology theories. In developing these pathways to engagement, we consider the norms and needs that govern the AI healthcare domain, and we evaluate incentives for maintaining the status quo against economic, legal, and social incentives for behavior change in line with transparency and fairness values.

This article examines the challenges and opportunities that arise with artificial intelligence (AI) and machine learning (ML) methods and tools when implemented within cultural heritage institutions (CHIs), focusing on three selected Swedish case studies. The article centres on the perspectives of the CHI professionals who deliver that implementation. Its purpose is to elucidate how CHI professionals respond to the opportunities and challenges AI/ML provides. The three Swedish CHIs discussed here represent different organizational frameworks and have different types of collections, while sharing, to some extent, a similar position in terms of the use of AI/ML tools and methodologies. The overarching question of this article is what is the state of knowledge about AI/ML among Swedish CHI professionals, and what are the related issues? To answer this question, we draw on (1) semi-structured interviews with CHI professionals, (2) individual CHI website information, and (3) CHI-internal digitization protocols and digitalization strategies, to provide a nuanced analysis of both professional and organisational processes concerning the implementation of AI/ML methods and tools. Our study indicates that AI/ML implementation is in many ways at the very early stages of implementation in Swedish CHIs. The CHI professionals are affected in their AI/ML engagement by four key issues that emerged in the interviews: their institutional and professional knowledge regarding AI/ML; the specificities of their collections and associated digitization and digitalization issues; issues around personnel; and issues around AI/ML resources. The article suggests that a national CHI strategy for AI/ML might be helpful as would be knowledge-, expertise-, and potentially personnel- and resource-sharing to move beyond the constraints that the CHIs face in implementing AI/ML. Quantifying Culture: a study of AI and Cultural Heritage Collections

Many modern digital products use Machine Learning (ML) to emulate human abilities, knowledge, and intellect. In order to achieve this goal, ML systems need the greatest possible quantity of training data to allow the Artificial Intelligence (AI) model to develop an understanding of “what it means to be human”. We propose that the processes by which companies collect this data are problematic, because they entail extractive practices that resemble labour exploitation. The article presents four case studies in which unwitting individuals contribute their humanness to develop AI training sets. By employing a post-Marxian framework, we then analyse the characteristic of these individuals and describe the elements of the capture-machine. Then, by describing and characterising the types of applications that are problematic, we set a foundation for defining and justifying interventions to address this form of labour exploitation.

The widespread usage of machine learning systems and econometric methods in the credit domain has transformed the decision-making process for evaluating loan applications. Automated analysis of credit applications diminishes the subjectivity of the decision-making process. On the other hand, since machine learning is based on past decisions recorded in the financial institutions’ datasets, the process very often consolidates existing bias and prejudice against groups defined by race, sex, sexual orientation, and other attributes. Therefore, the interest in identifying, preventing, and mitigating algorithmic discrimination has grown exponentially in many areas, such as Computer Science, Economics, Law, and Social Science. We conducted a comprehensive systematic literature review to understand (1) the research settings, including the discrimination theory foundation, the legal framework, and the applicable fairness metric; (2) the addressed issues and solutions; and (3) the open challenges for potential future research. We explored five sources: ACM Digital Library, Google Scholar, IEEE Digital Library, Springer Link, and Scopus. Following inclusion and exclusion criteria, we selected 78 papers written in English and published between 2017 and 2022. According to the meta-analysis of this literature survey, algorithmic discrimination has been addressed mainly by looking at the CS, Law, and Economics perspectives. There has been great interest in this topic in the financial area, especially the discrimination in providing access to the mortgage market and differential treatment (different fees, number of parcels, and interest rates). Most attention has been devoted to the potential discrimination due to bias in the dataset. Researchers are still only dealing with direct discrimination, addressed by algorithmic fairness, while indirect discrimination (structural discrimination) has not received the same attention.

In the current social and technological scenario, the term digital is abundantly used with an apparently transparent and unambiguous meaning. This article aims to unveil the complexity of this concept, retracing its historical and cultural origin. This genealogical overview allows to understand the reason why an instrumental conception of digital media has prevailed, considering the digital as a mere tool to convey a message, as opposed to a constitutive conception. The constitutive conception places the digital phenomenon in the broader ground of media studies, and it considers digital technologies as an interface between the subject and the world. In this perspective, the media is not added to the experience of the person, but it shapes it from within on a cognitive, expressive and communicative level. The article makes use of two powerful examples to show the shortcomings of an instrumental conception of the digital, and to affirm the value of a constitutive conception for current media studies regarding digital interfaces.

Could translation be fully automated? We must first acknowledge the complexity, ambiguity, and diversity of natural languages. These aspects of natural languages, when combined with a particular dilemma known as the computational dilemma, appear to imply that the machine translator faces certain obstacles that a human translator has already managed to overcome. At the same time, science has not yet solved the problem of how human brains process natural languages and how human beings come to acquire natural language understanding. We will then distinguish between the task of translation and the responsibility of the translator. Thereafter, we will conduct a survey of the methods of machine translation (viz. RBMT, SMT, NMT, foundation models or large language models). These methods will then be critically evaluated both in general and relative to Bar-Hillel’s hypothesis about the impossibility of fully automatic, high-quality machine translation (Fahqmt). Some concluding remarks will be made about the scope, prospects, and limits of machine translation.

This paper provides the first comprehensive analysis of ethical issues raised by artificial intelligence (AI) in veterinary medicine for companion animals. Veterinary medicine is a socially valued service, which, like human medicine, will likely be significantly affected by AI. Veterinary AI raises some unique ethical issues because of the nature of the client–patient–practitioner relationship, society’s relatively minimal valuation and protection of nonhuman animals and differences in opinion about responsibilities to animal patients and human clients. The paper examines how these distinctive features influence the ethics of AI systems that might benefit clients, veterinarians and animal patients—but also harm them. It offers practical ethical guidance that should interest ethicists, veterinarians, clinic owners, veterinary bodies and regulators, clients, technology developers and AI researchers.

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies.

Digital systems are reshaping how we engage with music as a sounding dimension of cultural life that is capable of being transformed into a commodity. At the same time, as we increasingly engage through digital media with each other and with virtual others, attributes of music that underpin our capacity to interact communicatively are disregarded or overlooked within those media. Even before the advent of technologies of music reproduction, music was susceptible to assimilation into economic acts of exchange. What is new in the digital world is the way in which modes of engagement with music are themselves being absorbed into an economy built on the datafication of virtual acts and the digital shadows of casual preferences. But music is more than just sounds that are culturally sanctioned as musical. Music is manifested as behaviours, and in interactive behaviour. Music is participatory as well as presentational, and in the participatory mode—involving collective, non-specialist, interactive real-time music-making—has significant individual and social consequences. Yet music as real-time participation is largely absent from the virtual world, with potential social costs that remain to be understood. Moreover, our everyday, face-to-face communicative—conversational—interactions are imbued with patterns between interlocutors that are musical, in that they share features with what we are happy to describe as “music”. These features are presently lacking in digital systems designed to subserve communicative functions, and this paper will consider the significant implications for our interactions with machines to which their successful incorporation into voice–user interfaces would give rise.

The world is currently facing a wave of data centre construction. Fuelled by an explosion of data production and the emergence of edge computing, our cities are witnessing the materialisation of new architectural typologies that increasingly convolute notions of digital and bodily distinction. Whilst the last 2 decades have seen the proliferation of separate human and post-human urban environments, here we consider the agency and performativity of human communities within increasingly tangled contexts. As edge computing continues to bring the material reality of data processing closer to our physical bodies, perhaps its time to reassess who, or what, our cities now serve. In an age of unprecedented digital transformation, when our identities seem to be ever more intangible, how we might perform within environments that are increasingly catering to our digital rather than bodily needs? Today, the Anthropocene expels nature to the periphery. Tomorrow, humans become marginalised—“weeds”, thriving in an epoch of data.

This article is about professional skill and what happens when work is instrumented with technology. The purpose is to contribute to the understanding of the professional skill, its role and development in an increasingly digitalized working life. The article also argues that more research is needed to understand what is at stake in terms of professional skill in the age of digital technology. The research on which the article is based shows that people adapt their way of thinking and perceiving reality to the technology they use. This means that people are gradually becoming more and more like machines. There is an ongoing intellectual inner mechanization, which can be contrasted with the outer mechanization of human muscle power that the industrial revolution entailed. The intellectually mechanized man observes and describes reality in the terms of technology and loses the ability to discern nuances and make qualified judgments gradually. The concepts of Turing’s man and functional autism capture these phenomena. Tacit engagement is a concept that captures the tacit knowledge that can only be expressed when people share physical space. The concept draws attention to the importance of the physical space and the body and what is at stake in terms of interpersonal knowledge in the wake of digital communication technology. It is not machines with supposedly human abilities and characteristics that we need to pay attention to when working life becomes increasingly digitalized, but people who gradually become like machines. What is required to safeguard the knowledge that is unique to man is bildung, i.e., to see the limits of the technology and the abstract theoretical models one uses. Art, classical literature, and drama, with their more plastic language, can reach areas where mathematics and natural science cannot reach.

AI and robotic technologies attract much hype, including utopian and dystopian future visions of technologically driven provision in the health and care sectors. Based on 30 interviews with scientists, clinicians and other stakeholders in the UK, Europe, USA, Australia, and New Zealand, this paper interrogates how those engaged in developing and using AI and robotic applications in health and care characterize their future promise, potential and challenges. We explore the ways in which these professionals articulate and navigate a range of high and low expectations, and promissory and cautionary future visions, around AI and robotic technologies. We argue that, through these articulations and navigations, they construct their own perceptions of socially and ethically ‘acceptable futures’ framed by an ‘ethics of expectations.’ This imbues the envisioned futures with a normative character, articulated in relation to the present context. We build on existing work in the sociology of expectations, aiming to contribute towards better understanding of how technoscientific expectations are navigated and managed by professionals. This is particularly timely since the COVID-19 pandemic gave further momentum to these technologies.