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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]

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]

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]

In this paper, I shall set out the pros and cons of assigning legal personhood on artificial intelligence systems (AIs) under civil law. More specifically, I will provide arguments supporting a functionalist justification for conferring personhood on AIs, and I will try to identify what content this legal status might have from a regulatory perspective. Being a person in law implies the entitlement to one or more legal positions. I will mainly focus on liability as it is one of the main grounds for the attribution of legal personhood, like for collective legal entities. A better distribution of responsibilities resulting from unpredictably illegal and/or harmful behaviour may be one of the main reasons to justify the attribution of personhood also for AI systems. This means an efficient allocation of the risks and social costs associated with the use of AIs, ensuring the protection of victims, incentives for production, and technological innovation. However, the paper also considers other legal positions triggered by personhood in addition to responsibility: specific competencies and powers such as, for example, financial autonomy, the ability to hold property, make contracts, sue (and be sued). [ABSTRACT FROM AUTHOR]

The article examines the problem of ensuring the political stability of a democratic social system with a shortage of a vital commodity (like vaccine against COVID-19). In such a system, members of society citizens assess the authorities. Thus, actions by the authorities to increase the supply of this commodity can contribute to citizens' approval and hence political stability. However, this supply is influenced by random factors, the actions of competitors, etc. Therefore, citizens do not have sufficient information about all the possibilities of supplying, and it is difficult for them to make the right decisions. Such citizen unawareness can be exploited by unscrupulous politicians to achieve personal targets. Therefore, it is necessary to organize public control to motivate politicians to use all available opportunities in supplying. The goal of the paper is to build such a digital mechanism of public control of the politicians by citizens, which would best assess and stimulate the activities of the authorities to improve the supply of a vital commodity. In the age of artificial intelligence, such digital public control in the face of uncertainty can be based on digital machine learning. In addition, it is necessary to take into account and model the activities of politicians associated with the presence of their own targets that do not coincide with public ones. Such politicians can use the learning of citizens for their own targets. The objective of the article is to build an optimal digital mechanism of public control in a two-level model of a democratic social system—a digital society. At its top level, there is the Citizen, who gives an assessment for the Politico located at the lower level. In turn, the Politico can influence the supplying of a vital commodity. Political stability is guaranteed if the Citizen regularly approves of the Politico's actions to increase this supply. However, the Politico may not use the opportunities available to him to offer a commodity to achieve a personal target. To avoid this, the Politico's control mechanism is proposed. It includes the procedure for digital learning of the Citizen, as well as a procedure for assessing the Politico activity. Sufficient conditions have been found for the synthesis of such the Politico's control mechanism, at which stochastic possibilities of increasing the supply of a vital commodity are used. The example of such the Politico's control mechanism is considered on the case of supply of the COVID-19 vaccine in England. [ABSTRACT FROM AUTHOR]

Our fascination with artificial intelligence (AI), robots and sentient machines has a long history, and references to such humanoids are present even in ancient myths and folklore. The advancements in digital and computational technology have turned this fascination into apprehension, with the machines often being depicted as a binary to the human. However, the recent domains of academic enquiry such as transhumanism and posthumanism have produced many a literature in the genre of science fiction (SF) that endeavours to alter this antagonistic notion of AI. In his novel Klara and the Sun, Kazuo Ishiguro explores this notion of AI as a caring machine capable of nursing an ailing young girl back to health. Through this portrayal of AI as a sentient being capable of empathy and cognition, Ishiguro is attempting to usher in a new perception of AI: a posthuman perception that challenges the conventional notions of AI as a machine devoid of emotions. The novel further expands on the idea of self, soul and human consciousness and ponders on the question, what makes humans human, and if it is possible to imbibe these qualities onto an AI. Owing to the novelty of this notion, an unexplored avenue that deserves further exploration, this paper examines the plausibility of this new notion of AI through a careful exegesis of the novel. The paper also attempts to chart the impact of SF in society and culture. The study reveals a positive shift in perception towards AI, and there seems to be much scope for interdisciplinary and transdisciplinary research. [ABSTRACT FROM AUTHOR]

This paper presents an emerging aspect of intentionality through recent Artificial Intelligence (AI) developments in art and design. Our main thesis is that, if we focus just on the outcome of the artistic process, the intentionality of the artist does not have any relevance. Intention is measured as a result of actions regardless of whether they are human-based or not as long as there is an esthetical value intersubjectively acknowledged. In other words, what matters is the 'intentio' embedded in the work of art (the output) rather than the 'intendere' (the process). By considering the former aspect, intentionality becomes free of human intervention, therefore 'unintentional'. This thesis is supported through a range of examples related to AI in general and AI art in particular. However, in the case of a self-driving vehicle, the macro-goal of the system is fixed by humans, even though the AI system itself will take care of decomposing the macro-goal into a set of micro-goals and take the corresponding decisions. In the case of art, the system does not need to have any exterior goal to the extent that art is pure freedom of expression, as many philosophers have acknowledged. In this sense, the decisions of any AI system generating art or design are independent of humans and must be judged only on the ground of the final outcome. A work of art will be judged 'artistic' to the extent that humans will recognize an artistic intent in the work itself. In particular, the introduction of random processes makes an AI-generated artwork fully independent of human creativity. [ABSTRACT FROM AUTHOR]

More intellectual modesty, but also conceptual clarity is urgently needed in AI, perhaps more than in many other disciplines. AI research has been coined by hypes and hubris since its early beginnings in the 1950s. For instance, the Nobel laureate Herbert Simon predicted after his participation in the Dartmouth workshop that "machines will be capable, within 20 years, of doing any work that a man can do". And expectations are in some circles still high to overblown today. This paper addresses the demand for conceptual clarity and introduces precise definitions of "strong AI", "superintelligence", the "technological singularity", and "artificial general intelligence" which ground in the work by the computer scientist Judea Pearl and the psychologist Howard Gardner. These clarifications allow us to embed famous arguments from the philosophy of AI in a more analytic context. [ABSTRACT FROM AUTHOR]

In popular discussions, the nuances of AI are often abridged as "the algorithm", as the specific arrangements of machine learning (ML), deep learning (DL) and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness of algorithmic systems. Perhaps in response to concerns about "the algorithm" by individuals and governments, social media platforms utilize transparency initiatives and official statements, in part, to deflect official regulation. In the following paper, I draw from transparency initiatives and statements from representatives of Facebook and TikTok as case studies of how AI is embedded in these platforms, with attention to the promotion of AI content moderation as a solution to the circulation of problematic material and misinformation. This examination considers the complexity of embedded AI as a material-discursive apparatus, predicated on discursive techniques—what is seeable, sayable, knowable in a given time period—as well as the material arrangements—algorithms, datasets, users, platforms, infrastructures, moderators, etc. As such, the use of AI as part of the immensely popular platforms Facebook and TikTok demonstrates that AI does not exist in isolation, instead functioning as human–machine ensemble reliant on strategies of acceptance via discursive techniques and the changing material arrangements of everyday embeddedness. [ABSTRACT FROM AUTHOR]

In light of the growing need to pay attention to general public opinions and sentiments toward AI, this paper examines the levels of understandings amongst the Australian public toward the increased societal use of AI technologies. Drawing on a nationally representative survey of 2019 adults across Australia, the paper examines how aware people consider themselves to be of recent developments in AI; variations in popular conceptions of what AI is; and the extent to which levels of support for AI are liable to alter with additional exposure to information about AI. While a majority of respondents consider themselves to have little knowledge and familiarity with the topic of AI, the survey nevertheless finds considerable range of relatively 'plausible' basic understandings of what AI is. Significantly, repeated questioning highlights a willingness among many people to reassess their opinions once having received further information about AI, and being asked to think through issues relating to AI and society. These patterns remain relatively consistent, regardless of respondents' political orientation, income, social class and other demographic characteristics. As such, the paper concludes by considering how these findings provide support for the development of public education efforts to further enhance what might be termed 'public understanding of AI'. [ABSTRACT FROM AUTHOR]

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a 'soft governance' ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. [ABSTRACT FROM AUTHOR]

The question of the moral status of AI and the extent to which that status ought to be recognised by societal institutions is one that has not yet received a satisfactory answer from lawyers. This paper seeks to provide a solution to the problem by defending a moral foundation for the recognition of legal personhood for AI, requiring the status to be granted should a threshold criterion be reached. The threshold proposed will be bare, noumenal agency in the Kantian sense. Agency has been identified by Alan Gewirth as the source of the rights claims of our own species and, at risk of contradiction, is a foundation that must be expanded to all agents or else we contradict the foundation of our own rights. This is something that ought to be recognised through the granting of legal personhood to all noumenal agents by any system that requires such personhood for the enforcement of rights, or else the rule restricting legal personhood cannot be seen as a valid legal norm. Having laid out the case, the paper will move on to defend this natural law conception against the narrower definition of legal personhood proposed by Bryson et al. with regards to AI. It will argue that bare agency is a sufficient, though not necessary, criterion for the ascription of legal personhood in any system that sees the status as necessary for the ascription of legal rights. The paper will conclude by analysing the proposals currently making their way through the legislatures of the UK and European Union. They will be assessed for their compatibility with the claim that a functioning legal system necessarily must recognise the legal personhood of all noumenal agents regardless of their origins, and whether they are future-proofed for the possibility that AI may meet this threshold. [ABSTRACT FROM AUTHOR]

The AI and Society discourse has previously drawn attention to the ways that digital systems embody the values of the technology development community from which they emerge through the development and deployment process. Research shows how this effect leads to a particular treatment of gender in computer systems development, a treatment which lags far behind the rich understanding of gender that social studies scholarship reveals and people across society experience. Many people do not relate to the narrow binary gender options of male or female, and many people express their gender identity in much richer ways than the sex/gender binary female/woman and male/man Boolean terms will allow. We ask: are "born-digital" gendered datasets in digital systems experienced as marginalising by those who express their identity beyond the male/female binary? Case Study: Ireland. To answer this universal question, this paper presents the findings of an empirical case study of people in Ireland with diverse gender identities and expressions, and their experiences with public data systems and new technologies. In spite of great social changes in Ireland which have led to constitutional change in favour of LGBTQI + people, born-digital systems were experienced by respondents as embodying socio-cultural values which were no longer accepted in society at large. For many of the respondents, digital technologies routinely marginalise them in all kinds of ways. These systems keep alive violence and oppression long after civil rights have been enshrined in constitutional law. This study is just one example of the way assumptions about digital are disengaged from society-at-large. It is a call to arms to all who are passionate about socially-responsible technology. [ABSTRACT FROM AUTHOR]

This paper provides an early evaluation of Artificial Intelligence (AI) against COVID-19. The main areas where AI can contribute to the fight against COVID-19 are discussed. It is concluded that AI has not yet been impactful against COVID-19. Its use is hampered by a lack of data, and by too much data. Overcoming these constraints will require a careful balance between data privacy and public health, and rigorous human-AI interaction. It is unlikely that these will be addressed in time to be of much help during the present pandemic. In the meantime, extensive gathering of diagnostic data on who is infectious will be essential to save lives, train AI, and limit economic damages. [ABSTRACT FROM AUTHOR]

This article conducts a mixed-method content analysis of Chinese and American news media coverage of Google DeepMind's Go playing computer program, AlphaGo. Drawing on humanistic approaches to artificial intelligence, combined with an empirically rigorous content analysis, it examines the differences and overlap in coverage by the Chinese and American press in their accounts of AlphaGo, and its historic match with Korea's Lee Sedol in March, 2016. The event was not only followed intensely in China, but also made the front page of The New York Times. This article finds that the Chinese press was more likely than the American press to frame AlphaGo as non-threatening, which the authors attribute to cultural differences and the two countries' different understandings of Go. In addition to quantitatively identifying similarities and differences in the framing of AlphaGo, this paper also investigates the underlying and evolving contestations over what constitutes the "human" and the "machine." It concludes by discussing the implications of the study's findings as well as outlining avenues for further research. [ABSTRACT FROM AUTHOR]

This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u's meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes' view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in the first section, I explain Descartes' view about language and mind. To show that Turing bites the bullet with his imitation game and in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and Abelsons' Script Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartes' denial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms. [ABSTRACT FROM AUTHOR]

This paper explores the history of ELIZA, a computer programme approximating a Rogerian therapist, developed by Jospeh Weizenbaum at MIT in the 1970s, as an early AI experiment. ELIZA's reception provoked Weizenbaum to re-appraise the relationship between 'computer power and human reason' and to attack the 'powerful delusional thinking' about computers and their intelligence that he understood to be widespread in the general public and also amongst experts. The root issue for Weizenbaum was whether human thought could be 'entirely computable' (reducible to logical formalism). This also provoked him to re-consider the nature of machine intelligence and to question the instantiation of its logics in the social world, which would come to operate, he said, as a 'slow acting poison'. Exploring Weizenbaum's 20th Century apostasy, in the light of ELIZA, illustrates ways in which contemporary anxieties and debates over machine smartness connect to earlier formations. In particular, this article argues that it is in its designation as a computational therapist that ELIZA is most significant today. ELIZA points towards a form of human–machine relationship now pervasive, a precursor of the 'machinic therapeutic' condition we find ourselves in, and thus speaks very directly to questions concerning modulation, autonomy, and the new behaviorism that are currently arising. [ABSTRACT FROM AUTHOR]

This paper presents a simple model of an AI (artificial intelligence) arms race, where several development teams race to build the first AI. Under the assumption that the first AI will be very powerful and transformative, each team is incentivised to finish first-by skimping on safety precautions if need be. This paper presents the Nash equilibrium of this process, where each team takes the correct amount of safety precautions in the arms race. Having extra development teams and extra enmity between teams can increase the danger of an AI disaster, especially if risk-taking is more important than skill in developing the AI. Surprisingly, information also increases the risks: the more teams know about each others' capabilities (and about their own), the more the danger increases. Should these results persist in more realistic models and analysis, it points the way to methods of increasing the chance of the safe development of AI. [ABSTRACT FROM AUTHOR]