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This paper shares results from a pedagogical experiment that assigns undergraduates to "cheat" on a final class essay by requiring their use of text-generating AI software. For this assignment, students harvested content from an installation of GPT-2, then wove that content into their final essay. At the end, students offered a "revealed" version of the essay as well as their own reflections on the experiment. In this assignment, students were specifically asked to confront the oncoming availability of AI as a writing tool. What are the ethics of using AI this way? What counts as plagiarism? What are the conditions, if any, we should place on AI assistance for student writing? And how might working with AI change the way we think about writing, authenticity, and creativity? While students (and sometimes GPT-2) offered thoughtful reflections on these initial questions, actually composing with GPT-2 opened their perspectives more broadly on the ethics and practice of writing with AI. In this paper, I share how students experienced those issues, connect their insights to broader conversations in the humanities about writing and communication, and explain their relevance for the ethical use and evaluation of language models. [ABSTRACT FROM AUTHOR]

This article explores the topic of waste management from an artistic perspective. The authors, Diaa Ahmed Mohamed Ahmedien, Rachel Singel, and María Lorena Pradal, propose three different approaches to reprocessing waste: the natural approach, the biological approach, and the digital approach. These approaches involve using invasive plant species to make recycled paper, exploring the use of bioplastics made from organic polymers, and creating a digital artwork that addresses the issue of digital data waste. The authors aim to redefine the environmental role of artistic practices and promote environment-friendly artistic practices. [Extracted from the article]

This paper examines the paradoxical transparency involved in training machine-learning models. Existing literature typically critiques the opacity of machine-learning models such as neural networks or collaborative filtering, a type of critique that parallels the black-box critique in technology studies. Accordingly, people in power may leverage the models' opacity to justify a biased result without subjecting the technical operations to public scrutiny, in what Dan McQuillan metaphorically depicts as an "algorithmic state of exception". This paper attempts to differentiate the black-box abstraction that wraps around complex computational systems from the opacity of machine-learning models. It contends that the degree of asymmetry in knowledge is greater in the former than the latter. In the case of software systems, software codes are difficult to understand as only software experts with sufficient domain knowledge are equipped to formulate a sound critique. In contrast, the meanings of trained parameters in a machine-learning model are obscure even to the data scientists who configure and train the model. Hence, the asymmetry of knowledge lies only in how data examples are collected, the choice and configuration of machine-learning models, and the specification of features in model design. Under the trend of algorithmic decision-making proliferating with machine-learning heuristics, the paper contends that the more symmetric distribution of knowledge in machine learning could lead to a more transparent production process if proper policies are in place. [ABSTRACT FROM AUTHOR]

Kazuo Ishiguro's novel Klara and the Sun (2021) philosophizes on how in the current technologically saturated culture, the gradual evolution of the empathetic humanoids has, on one hand, problematized our normative notions of cognitive and affective categories, and on the other, has triggered an order of emotional uncanniness due to our reliance on hyperreal real objects for receiving solace and companionship. The novel may be conceived to be a commentary on the emerging discourse in the domain of cognitive and emotional computing that aspires to transform the inner life and social relationships of the human community. The novelty of the paper lies in its ability to showcase how Kazuo Ishiguro's Klara and the Sun (2021) creates a rupture in the existing research and literary narrative by critiquing the theoretical underpinnings of emotional computing that optimistically foresees a future where simulated empathetic minds will be able to decode the complexities of the human emotions. It discusses how literature turns into an apt tool to reflect on the limitations of the programmed machines to decode the elusiveness of the human mind that defies the one-to-one correlation between words, multiple connotations, and their underlying emotions. Through the lenses of the fictional narrative, the paper foregrounds how the concept of the social robot designed to offer empathy, care, and companionship turns into a failed project. The paper draws on critical perspectives from disposability theory, posthuman affect, and immaterial bodies to foreground the noncodified feature of affective experientialities that emerge as a result of the interface between humans and nonanimate beings. [ABSTRACT FROM AUTHOR]

This paper presents an intuitive explanation about why and how Rawlsian Theory of Justice (Rawls in A theory of justice, Harvard University Press, Harvard, 1971) provides the foundations to a solution for algorithmic bias. The contribution of the paper is to discuss and show why Rawlsian ideas in their original form (e.g. the veil of ignorance, original position, and allowing inequalities that serve the worst-off) are relevant to operationalize fairness for algorithmic decision making. The paper also explains how this leads to a specific MinMaxfairness solution, which addresses the basic challenges of algorithmic justice. We combine substantive elements of Rawlsian perspective with an intuitive explanation in order to provide accessible and practical insights. The goal is to propose and motivate why and how the MinMaxfairness solution derived from Rawlsian principles overcomes some of the current challenges for algorithmic bias and highlight the benefits provided when compared to other approaches. The paper presents and discusses the solution by building a bridge between the qualitative theoretical aspects and the quantitative technical approach. [ABSTRACT FROM AUTHOR]

There is a growing body of scholarship on the ethics of autonomous vehicles. Yet the ethical discourse has mostly been focusing on the behavior of the vehicle in accident scenarios. This paper offers a different ethical prism: the implications of the autonomous vehicle for human well-being. As such, it contributes to the growing discourse on the wider societal and moral implications of the autonomous vehicle. The paper is premised on the neo-Aristotelian approach which holds that as human beings, our well-being depends on developing and exercising our innate human capacities: to know, understand, love, be sociable, imagine, create and use our bodies and use our willpower. To develop and exercise these capacities, our environments need to provide a range of opportunities which will trigger the development and exercise of the capacities. The main argument advanced in the paper is that one plausible future of the autonomous vehicle—a future of single-rider autonomous vehicles—may effectively reduce the opportunities to develop and exercise our capacities to know, be sociable and use our willpower. It will therefore be bad for human well-being, and this provides us with a moral reason to resist this plausible future and search for alternative ones. [ABSTRACT FROM AUTHOR]

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]

Ethical morality is one of the significant issues in self-driving cars. The paper provides a newer approach to solve the ethical decision problems in self-driving cars until there is no concrete ethical decision to all problems. This paper gives a two-way approach to solve a problem, with first being the mapping of problem to the solution already known or which has a fixed set of solutions and action priorities defined to a problem previously. Now, if no solution is found or mapping is unsuccessful, then the second stage activates, where the solution from Deep Q-learning model is calculated. It estimates the best Q value and returns that solution or action which maximizes the reward at that instance. The reward function is designed with decreasing priorities and acts accordingly, where the users can change or define their priorities if needed. The case study and results show that the solution that is present in the paper will lead to solving ethical morality problems in self-driving cars up to a great extent. [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]

The original extended mind theory propagated by Clark and Chalmers (Analysis 58:7–19, 1998) refers to the idea that our minds do not simply live within our brains or bodies but extend into the material world. In other words, the extended mind refers to the externalization of cognitive processes into technology. Through the case study of the artistic performance of the android Alter inspired by the Japanese Shintoist ritual of Kagura this paper reconceptualizes the extended mind from a technological act of transfer towards the relational, spatial, and performative ontologies of the intersubjective encounters between humans and non-humans. In an effort to decolonize performance and curatorial practice the paper draws upon indigenous scholarship, feminist STS, and performance studies to overcome preconceived conceptualizations of affect, perception, and relationality. The fundamental question the paper explores is how does intersubjective relationality unfold through performance in space? Based upon a critique of the android Alter's past performance the paper concludes with a re-envisioned event score that employs a reconceptualized theory of the extended mind and aimed at a different kind of intersubjective relationality to emerge. [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]

Social media has revolutionized the way information is distributed throughout society, as folks continue to rely entirely on these apps for information on current events, health protocols, and socio-political discussions. However, these containers of knowledge do not appear in the same shape for every user; Algorithms, informed by capitalist agendas, determine what information sifts through its networks and to whom. Data scientists, researchers, and activists have dissected the hidden mechanics fueling these popular platforms, inciting critical conversations around the harmful biases embedded in algorithms. These studies often skim the surface of how these algorithms digitally marginalize people of color, if acknowledging it at all. Even fewer have attempted to examine the role these algorithms play in the social activism and digital community organizing happening amongst communities of color via social media. Referring to indigenous scholar Marisa Elena Duarte's book Network Sovereignty as a framework of thought, this paper roots itself in the notion that technology is an extension of the agenda utilizing it. Through dissecting the algorithmic structures of popular social platforms among communities of color, this paper examines how social media distributes information within its networks and how its encoded biases silence Black, brown, and indigenous voices. This paper also provides insight into how BIPOC content-creators, when informed on how the algorithms work, can use these platforms to their advantage and effectively facilitate socio-political discourse online; Changing the narrative of social networks from being yet another landscape of white supremacy to instead a communal canvas for radical change. [ABSTRACT FROM AUTHOR]

The digital transfer of the mind to a computer system (i.e., mind uploading) requires representing the mind as a finite sequence of bits (1s and 0s). The classic "stored-program computer" paradigm, in turn, implies the equivalence between program and data, so that the sequence of bits themselves can be interpreted as a program, which will be algorithmically executed in the receiving device. Now, according to a previous proof, on which this paper is based, a computational or algorithmic machine, however complex, cannot be free (in the sense of 'self-determined'). Consequently, a finite sequence of bits cannot adequately represent a free mind and, therefore, a free mind cannot be digitally transferred, quod erat demonstrandum. The impossibility of making this transfer, as demonstrated here, should be a concern especially for those who wish to achieve it. Since we intend this to be a rigorous demonstration, we must give precise definitions and conditions of validity. The most important part of the paper is devoted to explaining the meaning and reasonableness of these definitions and conditions (for example that being truly free means being self-determined). Special attention is paid, also, to the philosophical implications of the demonstration. Finally, this thesis is distinguished from other closely related issues (such as other possible technological difficulties to "discretize" the mind; or, whether it is possible to transfer the mind from one material support to another one in a non-digital way). [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]

There is a problematic tradition of dualistic and reductionist thinking in artificial intelligence (AI) research, which is evident in AI storytelling and imaginations as well as in public debates about AI. Dualistic thinking is based on the assumption of a fixed reality and a hierarchy of power, and it simplifies the complex relationships between humans and machines. This commentary piece argues that we need to work against the grain of such logics and instead develop a thinking that acknowledges AI–human interconnectedness and the complexity in such relations. To learn how to live better with AI in futures to come, the paper suggests an AI politics that turns to practices of serious attentiveness to help us re-imagine our machines and re-configure AI–human relations. [ABSTRACT FROM AUTHOR]

With the increasing degrees AIs and machines, the need for implementing ethics in AIs is pressing. In this paper, we first survey current approaches to moral AIs and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral AIs can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making capacity of an AI based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that embraces different moral rules for making explicit moral decisions. The second is a machine learning system that accounts for calculating the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as it better addresses the moral disagreement problem of the top-down approach by including distinct moral principles. Besides, the pluralist hybrid system reduces the opacity of ethical decision-making by implementing explicit moral principles for moral decision-making. [ABSTRACT FROM AUTHOR]

Despite resistance from various societal actors, the development and deployment of lethal autonomous weaponry to warzones is perhaps likely, considering the perceived operational and ethical advantage such weapons are purported to bring. In this paper, it is argued that the deployment of truly autonomous weaponry presents an ethical danger by calling into question the ability of such weapons to abide by the Laws of War. This is done by noting the resonances between battlefield target identification and the process of ontic-ontological investigation detailed in Martin Heidegger's Being and Time, before arguing that the nature of lethal autonomous weaponry precludes them from being able to engage in such investigations—a key requisite for abiding by the relevant legislation that governs battlefield conduct. [ABSTRACT FROM AUTHOR]

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

In response to the ever-increasing spread of online disinformation and misinformation, several human–computer interaction tools to enhance data literacy have been developed. Among them, many employ elements of gamification to increase user engagement and reach out to a broader audience. However, there are no systematic criteria to analyze their relevance and impact for building fake news resilience, partly due to the lack of a common understanding of data literacy. In this paper we put forward an operationalizable definition of data literacy as a form of multidimensional critical thinking. We then survey 22 existing tools and classify them according to a framework of 10 criteria pointing to their gameful design and educational features. Through a comparative/contrastive analysis informed by a focus group, we provide a principled set of guidelines to develop more efficient human–computer interaction tools to teach how to critically think in the current media ecosystem. [ABSTRACT FROM AUTHOR]

Although social robots are achieving increasing prominence as companions and carers, their status as partners in an interactive relationship with humans remains unclear. The present paper explores this issue, first, by considering why social robots cannot truly qualify as "Thous", that is, as surrogate human partners, as they are often assumed to be, and then by briefly considering why it will not do to construe them as mere machines, slaves, or pets, as others have contended. Having concluded that none of these familiar designations does justice to social robots' still evolving and yet-to-be-defined status, I go on to consider whether engaging in a "relational turn" which prioritises the relationship over the entities in relation, can provide a more satisfactory alternative. In defending this stance, Damiano and Dumouchel (HUMANA.MENTE J Philos Stud 13:181–206, 2020) contend that in addition to foregrounding the possibility that social robots constitute new types of artificial companions which we can find "companionable in different ways", the relational turn has the added advantage of providing a more expansive and productive ethical framework for future research and development in this domain. But on balance, it is far from clear that this approach can circumvent the so-called "dummy-human" problem, the contention that social robots are little more than sophisticated toys with the potential to deceive their users about the prospects for genuine partnership and bonding. Accordingly, I conclude that while Damiano and Dumouchel's shift in "target phenomenology" brings to the fore facets of the interaction that may be difficult to infer by focussing on the relata alone, it is ultimately too "thin" to qualify as a template for robust human-robotic interaction of the sort that could qualify as a genuine partnership. Hence, while their relational turn has its merits in alerting us to the novel possibilities and challenges afforded by this ever-evolving technology and to the limitations of existing frameworks, it is contended that in itself it cannot provide an adequate template for conceptualising what a genuine human–robot partnership might entail. Instead, we need a more expansive approach that can do greater justice to the complexities of human-robotic interaction in its continuities as well as differences from more familiar human (and animal) prototypes. [ABSTRACT FROM AUTHOR]

Public entities around the world are increasingly deploying artificial intelligence (AI) and algorithmic decision-making systems to provide public services or to use their enforcement powers. The rationale for the public sector to use these systems is similar to private sector: increase efficiency and speed of transactions and lower the costs. However, public entities are first and foremost established to meet the needs of the members of society and protect the safety, fundamental rights, and wellbeing of those they serve. Currently AI systems are deployed by the public sector at various administrative levels without robust due diligence, monitoring, or transparency. This paper critically maps out the challenges in procurement of AI systems by public entities and the long-term implications necessitating AI-specific procurement guidelines and processes. This dual-prong exploration includes the new complexities and risks introduced by AI systems, and the institutional capabilities impacting the decision-making process. AI-specific public procurement guidelines are urgently needed to protect fundamental rights and due process. [ABSTRACT FROM AUTHOR]

Modern democratic states are increasingly adopting new information and communication technologies to enhance the efficiency and quality of public administration, public policy and services. However, there is substantial variation in the extent to which countries are successful in pursuing such public digitalization. This paper zooms in on the role of social trust as a possible account for the observed empirical pattern in the range and scope of public digitalization across countries. Our argument is that high social trust makes it easier to digitalize the public sector to the benefit of overall society. We develop a simple game-theoretical model that specifies this positive externality from a high-trust culture and provide empirical evidence that a higher level of social trust is associated with a higher level of public digitalization. Of course, high-trust countries cannot do without the rule of law (formal rules of the game) and some institutionalized mechanisms of control and accountability. However, from our argument, it follows that one future challenge could be that control crowds out trust, so that the balance is no longer optimal. If such overcontrol occurs, public digitalization win–win situations between citizens and government can be undermined, even in high-trust countries. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness. [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]

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. [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]

Advances in artificial intelligence and robotics stand to change many aspects of our lives, including our values. If trends continue as expected, many industries will undergo automation in the near future, calling into question whether we can still value the sense of identity and security our occupations once provided us with. Likewise, the advent of social robots driven by AI, appears to be shifting the meaning of numerous, long-standing values associated with interpersonal relationships, like friendship. Furthermore, powerful actors' and institutions' increasing reliance on AI to make decisions that may affect how people live their lives may have a significant impact on privacy while also raising issues about algorithmic transparency and human control. In this paper, building and expanding on previous works, we will look at how the deployment of Artificial Intelligence technology may lead to changes in identity, security, and other crucial values (such as friendship, fairness, and privacy). We will discuss what challenges we may face in the process, while critically reflecting on whether such changes may be desirable. Finally, drawing on a series of considerations underlying virtue ethics, we will formulate a set of preliminary suggestions, which—we hope—can be used to more carefully guide the future roll out of AI technologies for human flourishing; that is, for social and moral good. [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]

Machine learning algorithms (ML) are increasingly used to support decision-making in the exercise of public authority. Here, we argue that an important consideration has been overlooked in previous discussions: whether the use of ML undermines the democratic legitimacy of public institutions. From the perspective of democratic legitimacy, it is not enough that ML contributes to efficiency and accuracy in the exercise of public authority, which has so far been the focus in the scholarly literature engaging with these developments. According to one influential theory, exercises of administrative and judicial authority are democratically legitimate if and only if administrative and judicial decisions serve the ends of the democratic law maker, are based on reasons that align with these ends and are accessible to the public. These requirements are not satisfied by decisions determined through ML since such decisions are determined by statistical operations that are opaque in several respects. However, not all ML-based decision support systems pose the same risk, and we argue that a considered judgment on the democratic legitimacy of ML in exercises of public authority need take the complexity of the issue into account. This paper outlines considerations that help guide the assessment of whether a ML undermines democratic legitimacy when used to support public decisions. We argue that two main considerations are pertinent to such normative assessment. The first is the extent to which ML is practiced as intended and the extent to which it replaces decisions that were previously accessible and based on reasons. The second is that uses of ML in exercises of public authority should be embedded in an institutional infrastructure that secures reason giving and accessibility. [ABSTRACT FROM AUTHOR]

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is increasingly used to enhance production systems and meet the requirements of a rapidly evolving manufacturing environment. Compared to larger companies, however, small- and medium-sized enterprises (SMEs) lack in terms of resources, available data and skills, which impedes the potential adoption of analytics solutions. This paper proposes a preliminary yet general approach to identify low-cost analytics solutions for manufacturing SMEs, with particular emphasis on ML. The initial studies seem to suggest that, contrarily to what is usually thought at first glance, SMEs seldom need digital solutions that use advanced ML algorithms which require extensive data preparation, laborious parameter tuning and a comprehensive understanding of the underlying problem. If an analytics solution does require learning capabilities, a 'simple solution', which we will characterise in this paper, should be sufficient. [ABSTRACT FROM AUTHOR]

Fitness apps on mobile devices are gaining popularity, as more people are engaging in self-tracking activities to record their status of fitness and exercise routines. These technologies also evolved from simply recording steps and offering exercise suggestions to an integrated lifestyle guide for physical wellbeing, thus exemplify a new era of "quantified self" in the context of health as individual responsibility. There is a considerable amount of literature in science, technology and society (STS) studies looking at this phenomenon from different perspectives, linking it with the sociology of self-surveillance and neoliberal regimes of health. However, the human-technology interface, through which the micro- (behavioral) and macro- (social) aspects converge, still calls for extensive examination. This paper approaches this topic from the postphenomenological perspective, in combination with empirical studies of design analysis and interviews of fitness apps, to reveal the human-technology link between the design elements and people's perception through the direct experiences and interpretations of technology. It argues that the intentionality of self-tracking fitness app designs mediates the human-technology relations by "guiding" people into a quantified knowledge regime. It shapes the perceptions of fitness and health with representations of meanings about a "good life" of individual success and management. This paper also gives a critique of current individual, performance-oriented fitness app designs and offers the possibility of seeking alternatives through the multistable nature of human-technology relations—how altering interpretation and meaning of the design with a cultural or social context could change the form of technological embodiment. [ABSTRACT FROM AUTHOR]

Turing's much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing's 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing's 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. [ABSTRACT FROM AUTHOR]

A central issue in postphenomenology is how to explain the multistability of technologies: how can it be that specific technologies can be used for a wide variety of purposes (the "multi"), while not for all purposes (the "stability")? For example, a table can be used for the purpose of sleeping, having dinner at, or even for staging a fencing match, but not for baking a cake. One explanation offered in the literature is that the (material) design of a technology puts constraints on the purposes for which technologies can be used. In this paper, I argue that such an explanation—while partly correct—fails to address the role of the environment in which human beings operate in putting constraints on technology use. I suggest that James Gibson's affordance theory helps highlighting how stabilities in technology use arise in the interaction between human being and environment. Building on more recent approaches in affordance theory, I suggest that the environment can be conceptualized as a "rich landscape of affordances" that solicits certain actions, which are not just cued by the environment's material structure, but also by the normativity present in the form of life in which a human being participates. I briefly contrast the approach to affordances developed in this paper with how Klenk (2020) and Tollon (2021) have conceptualized the "affordance character" of technological artifacts, and highlight how a focus on the situated nature of affordances augments these earlier conceptualizations. [ABSTRACT FROM AUTHOR]

This paper discusses a version of the hermeneutic philosophy of science. Special focus is placed on the ways of reading theoretical objects in scientific inquiry. In implementing readable technologies, this reading succeeds in contextually visualizing the theoretical objects by means of various sorts of signs. A configuration of readable technology accomplishes a further step. The configuration textualizes the contextually produced signs. Textualizing the reading of theoretical objects interlaces the meaningful articulation and objectification of scientific domains. The horizon of possibilities for textualizing is constantly shifting in the process of normal-scientific inquiry, and the shifting horizon plays the role of a hermeneutic fore-structuring of the outcomes of textualizing. The paper explores the importance of "material hermeneutics" for the contextual reading of theoretical objects. The conclusion is drawn that the hermeneutic study of the entanglement of technological artifacts with the outcomes from reading-as-textualizing requires the introduction of ontic-ontological difference. [ABSTRACT FROM AUTHOR]

I would like to introduce the notion of machine hermeneutics in this paper. The notion refers to hermeneutical activity performed by machines. Machines are now capable of making the very interpretive tasks, using artificial intelligence algorithms based on the technology of machine learning that used to be the exclusive domain of human beings. In making this claim, I am not talking about possible conscious machines of the future, but those existing here and now. With facial recognition algorithms, for example, machines are now performing routinely what must be regarded as hermeneutical analyses with astounding accuracy and power. Thus, machine hermeneutics supplements Don Ihde's notion of material hermeneutics. In the latter, it is still human beings who do the interpretation, through the lenses provided the natural sciences; in this case, the natural sciences, or the technology afforded by the sciences, intervene between the human being and the world. In machine hermeneutics, on the contrary, the intervening comes in two layers. On the one hand, there is the usual intervention that Ihde talks about, but on the other, the artificial intelligence algorithm performs its own kind of intervention and interpretation, presenting an already interpreted result to the human beings, who then perceive it through the aid of the usual intervention such as the normal eyeglasses. Then the paper discusses the problem of how to justify the kind of perception that undergoes this process. In what sense can it be said that the algorithm is performing the right action, i.e., one such that the process comes up with a right picture of the world? I contend that this does not merely consist of technical excellence for the technology involved, but also ethical excellence. The two cannot be considered one apart from the other. [ABSTRACT FROM AUTHOR]

This paper is addressed to recent theoretical discussions of the Anthropocene, in particular Bernard Stiegler's Neganthropocene (Open Universities Press, 2018), which argues: "As we drift past tipping points that put future biota at risk, while a post-truth regime institutes the denial of 'climate change' (as fake news), and as Silicon Valley assistants snatch decision and memory, and as gene-editing and a financially-engineered bifurcation advances over the rising hum of extinction events and the innumerable toxins and conceptual opiates that Anthropocene Talk fascinated itself with—in short, as 'the Anthropocene' discloses itself as a dead-end trap...". The objective of this paper is therefore twofold: (1) to discuss how the Anthropocene is appropriated to certain ideological discourses (paradoxically) to maintain the hegemony of precisely those systems of production that have most accelerated climate change etc.; (2) to consider how the factography of the Anthropocene is exploited in this process to mask the ideological character of industry-aligned "technocratic" environmental management. The paper is not concerned with specific case studies in terms of government and industry policy, or climate science, but rather with the ways in which the discourse of the Anthropocene has been inflected within the humanities and the broader cultural field—that is to say, ideologically, as a system or logic of meaning. How the Anthropocene "means" is, in this respect, a question of some importance. This paper does not attempt to address all the facets of this question, but focuses upon a central "apocalyptic" strain in the discourse of the Anthropocene drawn particularly from Francis Fukuyama's millennial posthumanism and centred in the question of "sustainability" as catastrophe management—with the risk that real environmental degradation will become an alibi for a revived neoliberalism. In other words, that the critical Earth system transformations that characterise the Anthropocene are themselves commodities, and that the project of their amelioration is in process of defining a future (opportunistic) "crisis" rhetoric with a global political franchise. The ideological import of the Anthropocene stems precisely from the fact that it is planetary and, while catalysed by human agency, independent in its specific behaviour from it. The Anthropocene objectively presents as the contemporary counterpart of the Cold War doctrine of Mutually Assured Destruction and the most compelling argument for a new kind of technological "arms race." But it also presents as the condition of an emerging ideological discourse which will determine how this race is run. From the discourse on "energy security" to the widespread "security crackdown" on environmental activists across the so-called developed & developing world, the Anthropocene has come to represent the co-option of a scientific factography for the thinly disguised resurgence of "ideological science" of the Fukuyamaesque variety (post-history, post-human). For Fukuyama, the true meaning of "posthuman" is thus the accomplishment of humanity's historical mission. As the "End of History" designates an end of ideological struggle, so too the dénouement of the Anthropocene and the "ends of man" represent the accomplished purpose of species warfare: dominion, not simply over the world, but over all possible worlds. According to this narrative, science—like technology—must be uniquely at the service of the maintenance of the global order, organised around a universal appeal to "crisis management." It is precisely for this reason that what calls itself post-human masks the return of an ever-more-apocalyptic Humanism. [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]

Complex systems are difficult to manage, operate and maintain. This is why we see teams of highly specialised engineers in industries such as aerospace, nuclear and subsurface. Condition based monitoring is also employed to maximise the efficiency of extensive maintenance programmes instead of using periodic maintenance. A level of automation is often required in such complex engineering platforms in order to effectively and safely manage them. Advances in Artificial Intelligence related technologies have offered greater levels of automation but this potentially pivots the weight of decision making away from the operator to the machine. Implementing AI or complex algorithms into a platform can mean that the Operators' control over the system is diminished or removed altogether. For example, in the Boeing 737 Air Max Disaster, AI had been added to a platform and removed the operators' control of the system. This meant that the operator could not then move outside the extremely reserved, algorithm defined, "envelope" of operation. This paper analyses the challenges of AI driven condition based monitoring where there is a potential to see similar consequences to those seen in control engineering. As the future of society becomes more about algorithm driven technology, it is prudent to ask, not only whether we should implement AI into complex systems, but how this can be achieved ethically and safely in order to reduce risk to life. [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]

Recent simulations by Scanlon et al. showed seemingly spectacular results for the Waymo self-driving vehicle in simulations of real accident situations. In this paper, it is argued that the selection criteria for accident situations must be modified in accordance with the relevant policy alternatives. While Scanlon et al. compare Waymo with old human-driven vehicles, it is argued here that the relevant policy question is whether we ought to use self-driven vehicles or human-driven vehicles in the future, which means that we need to consider whether other technological solutions, which are available but not broadly used in human-driven vehicles, could result in human-driven vehicles managing to avoid the same accidents. In this article, a proposal for a new standard of selection criteria is made. [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]

Citizen science (CS) projects have started to utilize Machine Learning (ML) to sort through large datasets generated in fields like astronomy, ecology and biodiversity, biology, and neuroimaging. Human–machine systems have been created to take advantage of the complementary strengths of humans and machines and have been optimized for efficiency and speed. We conducted qualitative content analysis on meta-summaries of documents reporting the results of 12 citizen science projects that used machine learning to optimize classification tasks. We examined the distribution of tasks between citizen scientists, experts, and algorithms, and how epistemic agency was enacted in terms of whose knowledge shapes the distribution of tasks, who decides what knowledge is relevant to the classification, and who validates it. In our descriptive results, we found that experts, who include professional scientists and algorithm developers, are involved in every aspect of a project, from annotating or labelling data to giving data to algorithms to train them to make decisions from predictions. Experts also test and validate models to improve their accuracy by scoring their outputs when algorithms fail to make correct decisions. Experts are mostly the humans involved in a loop, but when algorithms encounter problems, citizens are also involved at several stages. In this paper, we present three main examples of citizens-in-the-loop: (a) when algorithms provide incorrect suggestions; (b) when algorithms fail to know how to perform classification; and (c) when algorithms pose queries. We consider the implications of the emphasis on optimization on the ideal of science and the role of citizen scientists from a perspective informed by Science and Technology Studies (STS) and Information Systems (IS). Based on our findings, we conclude that ML in CS classification projects, far from being deterministic in its nature and effects, may be open to question. There is no guarantee that these technologies can replace citizen scientists, nor any guarantee that they can provide citizens with opportunities for more interesting tasks. [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]

In this paper, I describe my artistic project, Igùn—a StyleGAN series trained to animate the research question: what bronze objects could have been produced should the 1897 British invasion not have occurred in the Benin Kingdom? In addition to looting over 3000 palace-commissioned artworks, I surmise that the invasion resulted in a 17-year (1897–1914) artistic decline. Although post-invasion colonial reports referred to a thriving art scene and increased colonial art patronage, there is a dearth of visual documentation to identify objects created during this period. Considering this absence, I propose Igùn, a series of StyleGAN models trained on a dataset of looted Benin Bronzes. This project is informed by the Igún Eronmwon's (the royal guild of bronze casters) artistic protocols. Finally, I present three prototypes based on emergent themes—infancy and facial expressions, which were underexplored in Benin's classical bronze casting tradition. [ABSTRACT FROM AUTHOR]