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The purpose of this paper is to discuss the responsibility of AI experts for guiding the development of AI in a desirable direction. More specifically, the aim is to answer the following research question: To what extent are AI experts responsible in a forward-looking way for effects of AI technology that go beyond the immediate concerns of the programmer or designer? AI experts, in this paper conceptualised as experts regarding the technological aspects of AI, have knowledge and control of AI technology that non-experts do not have. Drawing on responsibility theory, theories of the policy process, and critical algorithm studies, we discuss to what extent this capacity, and the positions that these experts have to influence the AI development, make AI experts responsible in a forward-looking sense for consequences of the use of AI technology. We conclude that, as a professional collective, AI experts, to some extent, are responsible in a forward-looking sense for consequences of use of AI technology that they could foresee, but with the risk of increased influence of AI experts at the expense of other actors. It is crucial that a diversity of actors is included in democratic processes on the future development of AI, but for this to be meaningful, AI experts need to take responsibility for how the AI technology they develop affects public deliberation. [ABSTRACT FROM AUTHOR]

It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the holistic puzzle. To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. This is done by training seven supervised machine learning models on the compilation of data from the 2019 Special Eurobarometer on undeclared work and relevant figures from other sources. The conducted analysis not only does attest to the completeness of our knowledge concerning the drivers of undeclared work but also paves the way for wide usage of artificial intelligence in monitoring and confronting this detrimental practice. The study, however, exposes the necessity of having at disposal considerably larger datasets compared to those currently available if successful real-world applications of machine learning are to be achieved in this field. Alongside the apparent theoretical contribution, this paper is thus also expected to be of particular importance for policymakers, whose efforts to tackle tax evasion will have to be expedited in the period after the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This paper delves into the intricate relationship between Artificial Intelligence (AI) and the art ecosystem, emphasizing the need for a decolonizing approach in the face of AI's growing influence. It argues that the development of AI is not just a technological leap but also a significant cultural and societal moment, akin to the advent of moving images that Walter Benjamin famously analyzed. The paper examines how AI, particularly in its current oligarchical and corporate-driven form, perpetuates and magnifies the existing social inequalities, thereby necessitating a critical and radical rethinking of its role in society and the arts. At the heart of the discussion is the concept of AI as a broad term encompassing various forms of machine intelligence, from natural language processing to computer vision. The paper criticizes the dominant anthropocentric view of intelligence and creativity, proposing a more inclusive approach that considers the diverse forms of intelligence present in other species and potentially in AI itself. It underscores the role of AI in shaping the art ecosystem, not just in the creative process but also in gatekeeping and decision-making. The paper proposes a framework for decolonizing AI in the art ecosystem, focusing on four key tasks: recognizing access as a form of power, understanding and addressing biases inherent in AI, assessing the impact of AI on marginalized communities, and challenging dominant narratives and epistemologies to create space for alternative voices and perspectives. It emphasizes the need for artists and the art community to engage actively with AI, shaping its development towards more equitable and just outcomes. In conclusion, the paper calls for a radical reimagination of AI's role in society and the arts, advocating for a future where AI is not just about technological advancement but also about fostering a more inclusive, equitable, and creatively diverse world. It invites artists, thinkers, and innovators to join in this journey of reimagining and reshaping the future of AI and the art ecosystem. [ABSTRACT FROM AUTHOR]

In today's age, we see the increasing influence of technology on people, which begs to raise the question: "Is society determined by technology?" Rising up within the constraints of each society, technology had its limitations, as it catered to the needs and interests of the masses. As society evolved, so did its requirements. We are at a stage where dependence on technology has gone through the roof with new innovations coming up in the sector, the rise of artificial intelligence, for instance. Technology has replaced the concept of being a mere tool and is creating/demanding space of its own. With advancements in various fields like robotics which churn out machines that can mimic human behaviour and are equipped with anthropomorphic features, it has become rather difficult to view them as mere commodities waiting for one's (human master's) command. Keeping this in view, the paper primarily focuses on the relationship between society and technology, with special emphasis on artificial intelligence, through an analysis of the films Ra One (Bollywood), Android Kunjappan (Mollywood), and Ironman 3 (Hollywood). Secondarily, an experimental study was carried out to know the perception of the Engineering students, the future technologists, who are the stakeholders of the 21st-century technological world, with an aim to know their perception of technology and bring an awareness of the limitations of technology and its role in the creation of utopian and dystopian world. Finally, the paper examines the consequence of technological determinism and autonomy from a colonial perspective and intends to bring preparedness among the students to perceive technology conscious of its limitation and bring harmony between society and technology that leads towards building a utopian world. [ABSTRACT FROM AUTHOR]

Alan Turing's "Computing Machinery and Intelligence," published in 1950, is one of the founding texts in the field of artificial intelligence (AI), although the term was not coined until 1958, 4 years after his death. From the treatment of human intelligence as computational and the brain as mechanical to the comparison of animals to machines to the disregard for the materiality of computers to programming as a stand-in for procreation to fiction-inspired science, many of the core tenets that have shaped the field of AI have their origins in Turing's paper. A close analysis of the paper exposes some of the problematic logic underlying these tenets that are now proving damaging for both society and the planet. [ABSTRACT FROM AUTHOR]

The concept of magic is frequently used to discuss technology, a practice considered useful by some with others arguing that viewing technology as magic precludes a proper understanding of technology. The concept of magic is especially prominent in discussions of artificial intelligence (AI) and machine learning (ML). Based on an anthropological perspective, this paper juxtaposes ML technology with magic, using descriptions drawn from a project on an ML-powered system for propulsion control of cargo ships. The paper concludes that prior scholarly work on technology has failed to both define magic adequately and use research into magic. It also argues that although the distinction between ML technology and magic is important, recognition of the similarities is useful for understanding ML technology. [ABSTRACT FROM AUTHOR]

The promises and risks of Artificial Intelligence permeate current policy statements and have attracted much attention by AI governance research. However, most analyses focus exclusively on AI policy on the national and international level, overlooking existing federal governance structures. This is surprising because AI is connected to many policy areas, where the competences are already distributed between the national and subnational level, such as research or economic policy. Addressing this gap, this paper argues that more attention should be dedicated to subnational efforts to shape AI and asks which themes are discussed in subnational AI policy documents with a case study of Germany's 16 states. Our qualitative analysis of 34 AI policy documents issued on the subnational level demonstrates that subnational efforts focus on knowledge transfer between research and industry actors, the commercialization of AI, different economic identities of the German states, and the incorporation of ethical principles. Because federal states play an active role in AI policy, analysing AI as a policy issue on different levels of government is necessary and will contribute to a better understanding of the developments and implementations of AI strategies in different national contexts. [ABSTRACT FROM AUTHOR]

This paper considers a host of definitions and labels attached to the concept of smart cities to identify four dimensions that ground a review of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic element for the future. Given the persisting disagreements around the definition of a smart city, the article identifies in these four dimensions a more stable reference framework within which ethical concerns can be clustered and discussed. Identifying these dimensions makes possible a review of the ethical implications of smart cities that is transversal to their different types and resilient towards the unsettled debate over their definition. [ABSTRACT FROM AUTHOR]

Stories are an important indicator of our vision of the future. In the case of artificial intelligence (AI), dominant stories are polarized between notions of threat and myopic solutionism. The central storytellers—big tech, popular media, and authors of science fiction—represent particular demographics and motivations. Many stories, and storytellers, are missing. This paper details the accounts of missing AI narratives by leading scholars from a range of disciplines interested in AI Futures. Participants focused on the gaps between dominant narratives and the untold stories of the capabilities, issues, and everyday realities of the technology. One participant proposed a "story crisis" in which these narratives compete to shape the public discourse on AI. Our findings indicate that dominant narratives distract and mislead public understandings and conceptions of AI. This suggests a need to pay closer attention to missing AI narratives. It is not simply about telling new stories, it is about listening to existing stories and asking what is wanted from AI. We call for realistic, nuanced, and inclusive stories, working with and for diverse voices, which consider (1) story-teller; (2) genre, and (3) communicative purpose. Such stories can then inspire the next generation of thinkers, technologists, and storytellers. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) tools used in employment decision-making cut across the multiple stages of job advertisements, shortlisting, interviews and hiring, and actual and potential bias can arise in each of these stages. One major challenge is to mitigate AI bias and promote fairness in opaque AI systems. This paper argues that the equal opportunity merit principle is an ethical approach for fair AI employment decision-making. Further, explainable AI can mitigate the opacity problem by placing greater emphasis on enhancing the understanding of reasonable users (employing organisations) and affected persons (employees and job candidates) as to the AI output. Both the equal opportunity merit principle and explainable AI should be integrated in the design and implementation of AI employment decision-making systems so as to ensure, as far as possible, that the AI output is arrived at through a fair process. [ABSTRACT FROM AUTHOR]

In this paper, we argue that because of the advent of Artificial Intelligence, the secret ballot is now much less effective at protecting voters from voting related instances of social ostracism and social punishment. If one has access to vast amounts of data about specific electors, then it is possible, at least with respect to a significant subset of electors, to infer with high levels of accuracy how they voted in a past election. Since the accuracy levels of Artificial Intelligence are so high, the practical consequences of someone inferring one's vote are identical to the practical consequences of having one's vote revealed directly under an open voting regime. Therefore, if one thinks that the secret ballot is at least partly justified because it protects electors against voting related social ostracism and social punishment, one should be morally troubled by how Artificial Intelligence today can be used to infer individual electors' past voting behaviour. [ABSTRACT FROM AUTHOR]

Choice architecture describes the environment in which choices are presented to decision-makers. In recent years, public and private actors have looked at choice architecture with great interest as they seek to influence human behaviour. These actors are typically called choice architects. Increasingly, however, this role of architecting choice is not performed by a human choice architect, but an algorithm or artificial intelligence, powered by a stream of Big Data and infused with an objective it has been programmed to maximise. We call this entity the autonomous choice architect. In this paper, we present an account of why artificial intelligence can fulfil the role of a choice architect and why this creates problems of transparency, responsibility and accountability for nudges. We argue that choice architects, be them autonomous computational systems or human-beings, at a most basic level select, from a range of designs, the design which is most likely to maximise a pre-determined objective. We then proceed to argue that, given the growing demand for targeted, personalised choice architecture and for faster, dynamic reconfigurations of choice architecture, as well as the ever-expanding pool of data from which feedback can be drawn, the role of the human choice architect is increasingly obscured behind algorithmic, artificially intelligent systems. We provide a discussion of the implications of autonomous choice architects, focusing on the importance of the humans who programme these systems, ultimately arguing that despite technological advances, the responsibility of choice architecture and influence remains firmly one human beings must bear. [ABSTRACT FROM AUTHOR]

At present, although artificial intelligence (AI) has made great progress in the aspects of control policy and operation, there is still no unified ethical decision mode, laws and regulations in ethical dilemma, which seriously affect application and development of artificial intelligence. Therefore, this paper studies and analyzes various ethical decision mode of AI system, then the current research status and advantages, deficiencies of these decision modes are analyzed and summarized. According to the characteristics of the current ethical decision modes and existing problems, the future implementation mode for the future ethical decision of artificial intelligence is explored and discussed. A hierarchical hybrid ethical decision mode will be proposed and designed, which has the advantages of the existing ethical decision modes. In addition, it will provide a few references for research, design and formulation of laws, regulations of AI. [ABSTRACT FROM AUTHOR]

The victories of the Go-playing artificial intelligence (AI) "AlphaGo" against professional player Lee Sedol in 2016 had a profound impact on public and academic perceptions of AI. This event shocked observers, as the ability of a machine to defeat a world champion human in a highly complex game seemed to indicate that a machine had achieved human-like—or more than human—intelligence. But why was AlphaGo so readily anthropomorphized by academic and non-academic audiences alike? Drawing from existing analyses of reactions to and arguments concerning AlphaGo and AI generally, this paper argues that "generative" cognitive science—a school of thought exemplified by the linguistic work of Noam Chomsky—offers two novel contributions to this subject. First, generativism sheds light on an irrational double standard in the study of the human mind in contrast to the study of non-cognitive systems—"methodological dualism"—which, I argue, has been transferred to evaluations of AlphaGo and other AI. Second, by exposing this irrational double standard in perceptions of AI, I employ generativism's more well-known arguments concerning the nature of human intelligence and its scientific study to the evaluation of AI, exposing deficient interpretations widely used in the case of AlphaGo and AI generally. [ABSTRACT FROM AUTHOR]

Artificial intelligence ethics requires a united approach from policymakers, AI companies, and individuals, in the development, deployment, and use of these technologies. However, sometimes discussions can become fragmented because of the different levels of governance (Schmitt in AI Ethics 1–12, 2021) or because of different values, stakeholders, and actors involved (Ryan and Stahl in J Inf Commun Ethics Soc 19:61–86, 2021). Recently, these conflicts became very visible, with such examples as the dismissal of AI ethics researcher Dr. Timnit Gebru from Google and the resignation of whistle-blower Frances Haugen from Facebook. Underpinning each debacle was a conflict between the organisation's economic and business interests and the morals of their employees. This paper will examine tensions between the ethics of AI organisations and the values of their employees, by providing an exploration of the AI ethics literature in this area, and a qualitative analysis of three workshops with AI developers and practitioners. Common ethical and social tensions (such as power asymmetries, mistrust, societal risks, harms, and lack of transparency) will be discussed, along with proposals on how to avoid or reduce these conflicts in practice (e.g., building trust, fair allocation of responsibility, protecting employees' autonomy, and encouraging ethical training and practice). Altogether, we suggest the following steps to help reduce ethical issues within AI organisations: improved and diverse ethics education and training within businesses; internal and external ethics auditing; the establishment of AI ethics ombudsmen, AI ethics review committees and an AI ethics watchdog; as well as access to trustworthy AI ethics whistle-blower organisations. [ABSTRACT FROM AUTHOR]

This paper considers recent experiments in art and artificial intelligence that crystallize around training algorithms to generate artworks based on datasets derived from the Western art historical canon. Over the last decade, a shift towards the rejection of canonicity has begun to take shape in art historical discourse. At the same time, algorithmically enabled practices in the US and Europe have emerged that entrench the Western canon as a locus and guarantor of aesthetic value. Operating within the epistemic framework of a "view from nowhere," this tendency in generative art inherits the coloniality of both art history and artificial intelligence. Producing "art histories from nowhere," this tendency conflates the conceptual category of visual art with the histories of Western cultural production. It reproduces a set of aesthetic values that entrench the mythology of the artist-genius and his imputed whiteness and masculinity; the extolment of innovation and novelty as self-evident virtues; disembodied Cartesian models of knowing and sensing; and the erasure of contributions that have been occluded from canonical visibility. As we encounter systems trained on particular visions of art history and of the artist, how might we remain attentive to the specific lens through which they are taught to see? This essay addresses that question by bringing the coloniality of recent experiments into view, bridging data feminisms and decolonial studies to formulate alternative visions of encounters between art and AI. [ABSTRACT FROM AUTHOR]

China communicates strategic narratives about artificial intelligence (AI) in digital media productions to create a shared meaning about its actions and its image in the global race to develop AI. Building upon the literature in emotions and strategic narratives, this study seeks to clarify which emotions are discursively used in China's international AI narratives, and their function and significance. Specifically, the study investigates emotion discourses in AI-focused videos disseminated on China's international broadcasting (CGTN YouTube channel). The analysis reveals that pride, hope and fear discourses have multiple functions in China's international AI narratives on CGTN. Hope is used to represent China as a key competitor in the global AI race, who is catching up to the US. China uses pride to showcase its advances in AI applications, to highlight its transformation of traditional industries, and to identity itself as equal to the US. Fear is used to frame US's perception of China's AI developments, to suggest their crisis of confidence, but also to refute the "China threat". Additionally, the fear discourse is used to deliberate anticipated risks, particularly on job loss and data privacy. These emotion discourses forge China's identity as a future global AI power. The paper then discusses how these findings contribute to key debates about Chinese discourse and media strategy. [ABSTRACT FROM AUTHOR]

The General Data Protection Regulation (GDPR) of the EU confirms the protection of personal data as a fundamental human right and affords data subjects more control over the way their personal information is processed, shared, and analyzed. However, where data are processed by artificial intelligence (AI) algorithms, asserting control and providing adequate explanations is a challenge. Due to massive increases in computing power and big data processing, modern AI algorithms are too complex and opaque to be understood by most data subjects. Articles 15 and 22 of the GDPR provide a modest regulatory framework for automated data processing by, among other things, mandating that data controllers inform data subjects about when it is being used, and its logic and ramifications. Nevertheless, due to the phrasing of the articles and the numerous exceptions they allow, doubts have arisen about their effectiveness. In this paper, we empirically evaluate the quality and effectiveness of AI disclosures as mandated by the GDPR. By means of an online survey (N = 835), we investigated how data subjects expect to be informed about the automated processing of their data. We then conducted a content analysis of the AI disclosures of N = 100 companies and organizations. The combined findings reveal that current GDPR-mandated disclosures do not meet the expectations and needs of data subjects. Explanations drawn up following the guidelines of the generic formulations of the GDPR differ widely and are often vague, incomplete and lack transparency. In our conclusions we identify a path towards standardizing and optimizing AI information notices. [ABSTRACT FROM AUTHOR]

Given the pervasiveness of AI systems and their potential negative effects on people's lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more "explainable" by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of "health", which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing "justificatory explanations" about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of "health", we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that "justificatory explanations" are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project's main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. [ABSTRACT FROM AUTHOR]

Human computer interaction (HCI) has grown into a mature field of research. With artificial intelligence (AI) finding ubiquitous applications, HCI research is now moving ahead towards accommodating and integrating these approaches. A part of the research community is of the opinion that HCI and AI are fundamentally opposed to each other: with AI-powered devices being demonic and humans are to lose in a race against them and HCI-oriented products being human centred being the flag bearers of the human race. In this paper, we look at and analyse this perspective of how HCI relates to AI and the potential HCI–AI integration. We suggest that humans evolve by having dynamic identities. We further discuss its implications on the AI critique and suggest some new perspectives on the possible integration of HCI and AI research which we hope would be useful for the community. [ABSTRACT FROM AUTHOR]