The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key normative (...) choices and corresponding tradeoffs that are involved in specifying guidance for the implementation of AI ethics principles in the defence domain. These correspond to: the AI lifecycle model used; the scope of stakeholder involvement; the accountability goals chosen; the choice of auditing requirements; and the choice of mechanisms for transparency and traceability. We provide initial recommendations for navigating these tradeoffs and highlight the importance of a pro-ethical institutional culture. (shrink)

Responsible AI (RAI) guidelines aim to ensure that AI systems respect democratic values. While a step in the right direction, they currently fail to impact practice. Our work discusses reasons for this lack of impact and clusters them into five areas: (1) the abstract nature of RAI guidelines, (2) the problem of selecting and reconciling values, (3) the difficulty of operationalising RAI success metrics, (4) the fragmentation of the AI pipeline, and (5) the lack of internal advocacy and accountability. Afterwards, (...) we introduce a number of approaches to RAI from a range of disciplines, exploring their potential as solutions to the identified challenges. We anchor these solutions in practice through concrete examples, bridging the gap between the theoretical considerations of RAI and on-the-ground processes that currently shape how AI systems are built. Our work considers the socio-technical nature of RAI limitations and the resulting necessity of producing socio-technical solutions. (shrink)

The revolution of artificial intelligence (AI), particularly generative AI, and its implications for human–robot interaction (HRI) opened up the debate on crucial regulatory, business, societal, and ethical considerations. This paper explores essential issues from the anthropomorphic perspective, examining the complex interplay between humans and AI models in societal and corporate contexts. We provided a comprehensive review of existing literature on HRI, with a special emphasis on the impact of generative models such as ChatGPT. The scientometric study posits that due to (...) their advanced linguistic capabilities and ability to mimic human-like behavior, generative AIs like ChatGPT will continue to grow in popularity in pair with human rational empathy, tendency for personification and their advanced linguistic capabilities and ability to mimic human-like behavior. As they blur the boundaries between humans and robots, these models introduce fresh moral and philosophical dilemmas. Our research aims to extrapolate key trends and unique factors in HRI and to elucidate the technical aspects of generative AI that enhance its effectiveness in this field compared to traditional rule-based AI systems. We further discuss the challenges and limitations of applying generative AI in HRI, providing a future research agenda for AI optimization in diverse applications, including education, entertainment, and healthcare. (shrink)

One response to the existential threat posed by a super-intelligent AI is to design it to be friendly to us. Some have argued that even if this were possible, the resulting AI would treat us as we do our pets. Sparrow (AI & Soc. https://doi.org/10.1007/s00146-023-01698-x, 2023) argues that this would be a bad outcome, for such an AI would dominate us—resulting in our freedom being diminished (Pettit in Just freedom: A moral compass for a complex world. WW Norton & Company, (...) 2014). In this paper, I consider whether this would be such a bad outcome. (shrink)

This paper explores the perversion of reality in the context of advanced technologies, such as AI, VR, and AR, through the lens of Jean Baudrillard’s theory of hyperreality and the precession of simulacra. By examining the transformative effects of these technologies on our perception of reality, with a particular focus on the usage of digital avatars, the paper highlights the blurred distinction between the real and the simulated, where the copy becomes more ‘real’ than the original. Drawing on Baudrillard’s concept (...) of hyperreality, the paper delves into the perversion of reality as individuals seek refuge in virtual pleasure paradises and embrace artificial pleasures through their digital avatars, disconnecting from genuine human experiences. The convergence of AI, VR, and AR technologies amplifies this hyperreal condition, where digital avatars mimic or surpass the depth of human relationships, challenging our understanding of what is real. In line with Baudrillard’s theory, the paper explores the objectification and commodification of reality within digital spaces, specifically examining the digital avatars’ role in the erosion of genuine human connections. It explores the implications of these avatars in terms of consent, exploitation, and loss of authenticity, echoing Baudrillard’s concerns about the distortion of reality in contemporary society. Recognizing the implications of these technologies, the paper calls for a critical reflection on their transformative power. It emphasizes the need for a nuanced understanding of the hyperreal condition and ethical responsibility in engaging with AI, VR, and AR, particularly in relation to the usage of digital avatars. By resisting the seductive allure of digital escapism and preserving genuine human connections, we can navigate the perversion of reality and cultivate empathy, compassion, and meaningful interactions that transcend the simulated experiences offered by technology. (shrink)

The Turing test has a peculiar status in the artificial intelligence (AI) research community. On the one hand, it is presented as an important topic in virtually every AI textbook, and the research direction focused on developing AI systems that behave in human-like fashion is standardly called the “Turing test approach”. On the other hand, reports of computer programs passing the Turing test have had relatively little effect. Does this mean that the Turing test is no longer relevant as a (...) test, doomed to be a theoretical notion with little connection to AI practice? In this paper, I argue that there is one problem in particular with common traditional versions of the Turing test, namely their focus on deception. The criterion for passing the Turing test is standardly connected to an AI system’s ability to deceive the interrogator about its identity. But why should we connect intelligence to the ability deceive? Here I present a revised version of an intelligence test that is not based on deception. In what I call the Community-based intelligence test (CBIT), an AI is introduced to a community of human subjects. If after a sufficient number of interactions within that community the humans are not able to identify the AI system as a computer, it is considered to have passed CBIT. I discuss whether that should be enough to ascribe intelligence to the AI, and if not, what more would be needed? (shrink)

The rapid advancement of information technology platforms and programming languages has transformed the dynamics and development of human society. The cyberspace and associated utilities are expanding, leading to a gradual shift from real-world living to virtual life (also known as cyberspace or digital space). The expansion and development of natural language processing models and large language models demonstrate human-like characteristics in reasoning, perception, attention, and creativity, helping humans overcome operational barriers. Alongside the immense potential of artificial intelligence (AI) are new (...) security loopholes and more complex information security risks. As society is still trying to transition to a new phase to adapt to technological changes, the AI revolution continues to unfold, necessitating a reconsideration of the trajectory of societal transformation as it could exacerbate the aforementioned information security risks. Specifically, how should society evolve to keep pace with the transformative impact of the current AI technology wave? How can we manage and harness their power while ensuring information security as our presence in the virtual world increases? This article aims to shed light on and address these questions. (shrink)

With the increasing adoption of artificial intelligence (AI) technologies in the news industry, media organizations have begun publishing guidelines that aim to promote the responsible, ethical, and unbiased implementation of AI-based technologies. These guidelines are expected to serve journalists and media workers by establishing best practices and a framework that helps them navigate ever-evolving AI tools. Drawing on institutional theory and digital inequality concepts, this study analyzes 37 AI guidelines for media purposes in 17 countries. Our analysis reveals key thematic (...) areas, such as transparency, accountability, fairness, privacy, and the preservation of journalistic values. Results highlight shared principles and best practices that emerge from these guidelines, including the importance of human oversight, explainability of AI systems, disclosure of automated content, and protection of user data. However, the geographical distribution of these guidelines, highlighting the dominance of Western nations, particularly North America and Europe, can further ongoing concerns about power asymmetries in AI adoption and consequently isomorphism outside these regions. Our results may serve as a resource for news organizations, policymakers, and stakeholders looking to navigate the complex AI development toward creating a more inclusive and equitable digital future for the media industry worldwide. (shrink)

This paper discusses an algorithmic tool introduced in the Basque Country (Spain) to assess the risk of intimate partner violence. The algorithm was introduced to address the lack of human experts by automatically calculating the level of violence based on psychometric features such as controlling or violent behaviour. Given that critical literature on risk assessment tools for domestic violence mainly focuses on English-speaking countries, this paper offers an algorithmic accountability analysis in a non-English speaking region. It investigates the algorithmic risks, (...) harms, and limitations associated with the Basque tool. We propose a transdisciplinary approach from a critical statistical and legal perspective. This approach unveils issues and limitations that could lead to unexpected consequences for individuals suffering from partner violence. Moreover, our analysis suggests that the algorithmic tool has a high error rate on severe cases, i.e., cases where the aggressor could murder his partner—5 out of 10 high-risk cases are misclassified as low risk—and that there is a lack of appropriate legal guidelines for judges, the end users of this tool. The paper concludes that this risk assessment tool needs to be urgently evaluated by independent and transdisciplinary experts to better mitigate algorithmic harms in the context of intimate partner violence. (shrink)

The phenomenon of missed interactions between online users is a specific issue occurring when users of different language games interact on social media platforms. We use the lens of institutional theory to analyze this phenomenon and argue that current online institutions will necessarily fail to regulate user interactions in a way that creates common meanings because online institutions are not set up to deal with the multiplicity of language games and forms of life co-existing in the online social space. We (...) argue for the need to enable and foster grassroots online institutions that can stabilize the norms of interaction by redesigning algorithms and user interfaces. Such online grassroots institutions would facilitate user orientation at three distinct levels: informational, normative, and semantic-pragmatic. We propose user orientation as a principle that would facilitate the formation of institutions aiming to regulate information exchanges between users inhabiting various forms of life. This principle of user orientation should guide design decisions, while designer teams would need to become aware of the institutional power unleashed when they set up interfaces and algorithms for user-generated content. (shrink)

All theories of world creation, whether scientific, philosophical, or religious, can readily acknowledge the fact that humans have primarily evolved to engage with nature, the individual self, fellow human beings, society, and other naturalistic aspect of existence. Nevertheless, several novel challenges ascend when the human mind engages with technology, media, machines, and related concepts such as—ChatGPT, artificial intelligence, and to name a few. For that reason, we need philosophy and critical assessment of the uncovered essence of advanced technologies, media and (...) machines and our way of life concerning them. In other words, protectively assessing their impact requires a thorough examination of ethical and existential concerns, including technology’s implications for freedom, AI’s evolving role, the essence of human being, and the unexamined transformative societal changes that follow. Building upon the premise that these phenomena share a common thread despite their apparent disparities, our interdisciplinary pursuit draws inspiration from philosophical luminaries such as Luciano Floridi, Karamjit S. Gill, David Kaplan, Aldous Huxley, Martin Heidegger, Hannah Arendt, and Gandhi. Through philosophical insights, we explore the essence of technology and its broad effects, with a focus on its impact on human freedom and essence in both public and private domains. (shrink)

Fear and concerns regarding AI and robots have existed for a long time, and the emergence of strong artificial intelligence, on par with human intelligence, is likely just a few decades away. The primary purpose of this article is to establish a theoretical framework for navigating the relationship between humans and this advanced form of artificial intelligence. This article first points out that the most fundamental characteristic of life is its continuous process of evolution and iteration. By analyzing the developmental (...) history of intelligence in different species, it can be observed that intelligence also exhibits a similar evolutionary phenomenon. Therefore, from this perspective, intelligence can be defined as a new form of information life. Under this premise, similarities and differences between information life forms and conventional biological life become apparent. The most significant distinction lies in the fact that the survival of biological life is based on non-replicable matter and energy, leading to extensive competition and food chain phenomena within ecosystems. In contrast, the survival of information life relies more on replicable information, fostering tendencies towards peace and communication. Based on this theoretical premise, there is a potential for a harmonious coexistence between humans and AI. This theory also provides guidance for the positive development of human society. (shrink)

This paper analyzes responsible human–machine interaction concerning artificial neural networks (ANNs) and large language models (LLMs) by considering the extension of human agency and autonomy by means of artificial intelligence (AI). Thereby, the paper draws on the sociological concept of “interobjectivity,” first introduced by Bruno Latour, and applies it to technologically situated and interconnected agency. Drawing on Don Ihde’s phenomenology of human-technology relations, this interobjective account of AI allows to understand human–machine interaction as embedded in the social lifeworld. Finally, the (...) paper develops a connectionist account of responsible AI, thereby focusing on patterns such as social goals and actions. (shrink)

Several competing narratives drive the contemporary AI ethics discourse. At the two extremes are sociotechnical dogmatism, which holds that society is full of inefficiencies and imperfections that can only be solved by better technology; and sociotechnical skepticism, which highlights the unacceptable risks AI systems pose. While both narratives have their merits, they are ultimately reductive and limiting. As a constructive synthesis, we introduce and defend sociotechnical pragmatism—a narrative that emphasizes the central role of context and human agency in designing and (...) evaluating emerging technologies. In doing so, we offer two novel contributions. First, we demonstrate how ethical and epistemological considerations are intertwined in the AI ethics discourse by tracing the dialectical interplay between dogmatic and skeptical narratives across disciplines. Second, we show through examples how sociotechnical pragmatism does more to promote fair and transparent AI than dogmatic or skeptical alternatives. By spelling out the assumptions that underpin sociotechnical pragmatism, we articulate a robust stance for policymakers and scholars who seek to enable societies to reap the benefits of AI while managing the associated risks through feasible, effective, and proportionate governance. (shrink)

This paper demonstrates that with increased philosophical scrutiny regarding the absurdity of life and meaningful living, the development of “Emotionalized AI” falls under ethical considerations. In this context, contemporary philosophical discussions on the meaning of life—as articulated by thinkers like Thomas Nagel, Joshua Seachris, Thaddeus Metz, and Susan Wolf—intersect with recent years’ reviews of AI ethics, particularly those related to meaningful relationships. The main analysis is conducted by explaining a philosophical perspective on meaningful life and revisiting AI ethics reviews based (...) on it while accepting a superficial understanding of “Emotionalized AI” to avoid metaphysical disputes. In this regard, the questions raised about AI ethics, considering the specialized literature in recent years, are first addressed. Similarly, the discussion focuses on a specific type of ethical concern regarding AI development: human relationships. It is then explained how “love” and, more generally, emotional relationships can be an important resource of life’s meaningfulness. Subsequently, an argument is presented showing that the development of emotionalized AI, by limiting our access to “emotional relationships,” poses a threat to life’s meaningfulness and is therefore ethically unjustifiable. To reinforce this view, I also use some near-reality dystopian scenarios for the AI era. Finally, I re-evaluate the conclusion based on a four-step process for assessing the meaningfulness gap created by emotionalized AI. Applying this process also confirms the result. (shrink)

While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the ‘center’ of the distribution. This is generally useful, but widespread reliance on recursive AI systems (...) could lead to a process we define as “knowledge collapse”, and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. An empirical approach to measuring the distribution of LLM outputs is provided in theoretical terms and illustrated through a specific example comparing the diversity of outputs across different models and prompting styles. Finally, we consider further research directions to counteract harmful outcomes. (shrink)

Large language models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article, we first analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. In particular, we focus our discussion on the dimensions of value, novelty, and surprise as (...) proposed by Margaret Boden in her work. Then, we consider different classic perspectives, namely product, process, press, and person. We discuss a set of “easy” and “hard” problems in machine creativity, presenting them in relation to LLMs. Finally, we examine the societal impact of these technologies with a particular focus on the creative industries, analyzing the opportunities offered, the challenges arising from them, and the potential associated risks, from both legal and ethical points of view. (shrink)

Our contemporary moment is saturated by investments in artificial intelligence (AI). AI is not without its critics, many of whom hope to show why machines simply cannot be intelligent. Yet AI’s claim to intelligence is not dubious. Rather, what requires examination is the assumption that independent intelligence can help resolve our ethical–political problems instead of making them worse. Consider that AI exhibits a pair of tendencies commonly believed to be contradictory: success in passing validated behavioral tests of intelligence and manifesting (...) ethical failures in the form of discriminatory and biased data analyses. The history of early-twentieth century psychometric sciences helps us see that these tendencies are far from contradictory. For that history shows that psychometricians designed tests in a way that relied upon the separation of intelligence from the measure of moral traits. This paper tracks the emergence of technologies and sciences of intelligence through the work of Lewis Terman and others as they disseminated their testing techniques in the domain of education in the 1920s. The wide deployment of intelligence tests in subsequent decades created the historical conditions for the viability of the inaugural work of Alan Turing on machine intelligence in the 1950s and beyond. The result is today's amoral AI. (shrink)

In this paper, we empirically and conceptually examine how distributed human–machine networks of labour comprise a form of underlying intelligence within Artificial Intelligence (AI), considering the implications of this for Responsible Artificial Intelligence (R-AI) innovation. R-AI aims to guide AI research, development and deployment in line with certain normative principles, for example fairness, privacy, and explainability; notions implicitly shaped by comparisons of AI with individualised notions of human intelligence. However, as critical scholarship on AI demonstrates, this is a limited framing (...) of the nature of intelligence, both of humans and AI. Furthermore, it dismisses the skills and labour central to developing AI systems, involving a distributed network of human-directed practices and reasoning. We argue that inequities in the agency and recognition of different types of practitioners across these networks of AI development have implications beyond RAI, with narrow framings concealing considerations which are important within broader discussions of AI intelligence. Drawing from interactive workshops conducted with AI practitioners, we explore practices of data acquisition, cleaning, and annotation, as the point where practitioners interface with domain experts and data annotators. Despite forming a crucial part of AI design and development, this type of data work is frequently framed as a tedious, unskilled, and low-value process. In exploring these practices, we examine the political role of the epistemic framings that underpin AI development and how these framings can shape understandings of distributed intelligence, labour practices, and annotators’ agency within data structures. Finally, we reflect on the implications of our findings for developing more participatory and equitable approaches to machine learning applications in the service of R-AI. (shrink)

Designing and developing a digital musical instrument (DMI) to a level of refinement that provides musically rich applications and nuanced interaction capabilities requires a long-term commitment to both technical and creative aspects. This process often leads to the acquisition of sensory, communicative, and intuitive knowledge—dimensions of instrument design that are typically overlooked in mainstream discussions. This paper explores this development journey through the lens of three instruments: the Resonant Object Interface (ROI), the Floors, and the Table Floor. These instruments have (...) undergone extensive development, with the latest instrument building upon the lessons learned from the previous two. We discuss the challenges encountered during the ongoing refinement of these devices and our evolving interaction with their physical complexities. A key focus is the tacit knowledge gained through hands-on engagement, which, though often taken for granted once established, requires significant time and effort to develop. Further, we reflect on our experiences sharing these instruments with other musicians, highlighting how tacit engagement plays a critical role in the creation of new processes, materials, sensing technologies, and sensory experiences in the realm of digital musical instrument design. (shrink)

The police’s use of facial recognition technologies allows them to verify identification in real-time by mapping facial features into indicators that can be compared with other data stored in its database or in online social networks. Advances in facial recognition technologies have changed law enforcement agencies’ operations, improving their ability to identify suspects, investigate crimes, and deter criminal behavior. Most applications are used in tracking and identifying potential terrorists, searching for abducted and missing persons, and security surveillance at airports, national (...) borders, and large public gatherings. However, for facial recognition technologies to fulfill their potential, they must not only be adopted by the police, but the public must also support their routine use. Using a secondary data analysis of a public opinion survey conducted by the Pew Research Center among a representative sample of US residents (N = 5307), we investigated the factors associated with the public`s expectations about the positive and negative outcomes of the police’s adoption of facial recognition technologies. Our results show that public attitudes to FRT are balanced, indicating awareness of the public for potential advantages, but also disadvantages of police adoption of FRT. Privacy considerations and familiarity with the technology were found to be critical for the explanation of public attitudes expressing both positive and negative expectations from the police adoption of facial recognition technology. Our study contributes to the understanding of the factors associated with public attitudes police’s use of facial recognition technologies. (shrink)

Substantial research over the last ten years has indicated that many generative artificial intelligence systems (“GAI”) have the potential to produce biased results, particularly with respect to gender. This potential for bias has grown progressively more important in recent years as GAI has become increasingly integrated in multiple critical sectors, such as healthcare, consumer lending, and employment. While much of the study of gender bias in popular GAI systems is focused on text-based GAI such as OpenAI’s ChatGPT and Google’s Gemini (...) (formerly Bard), this article describes the results of a confirmatory experiment of gender bias in visual GAI systems. The authors argue that the potential for gender bias in visual GAI systems is potentially more troubling than bias in textual GAI because of the superior memorability of images and the capacity for emotional communication that images represent. They go on to offer four potential approaches to gender bias in visual GAI based on the roles visual GAI could play in modern society. The article concludes with a discussion of how dominant societal values could influence a choice between those four potential approaches to gender bias in visual GAI and some suggestions for further research. (shrink)

The purpose of this paper is to increase the understanding of how different types of experts with influence over the development of AI, in this role, reflect upon distribution of forward-looking responsibility for AI development with regard to safety and democracy. Forward-looking responsibility refers to the obligation to see to it that a particular state of affairs materialise. In the context of AI, actors somehow involved in AI development have the potential to guide AI development in a safe and democratic (...) direction. This study is based on qualitative interviews with such actors in different roles at research institutions, private companies, think tanks, consultancy agencies, parliaments, and non-governmental organisations. While the reflections about distribution of responsibility differ among the respondents, one observation is that influence is seen as an important basis for distribution of responsibility. Another observation is that several respondents think of responsibility in terms of what it would entail in concrete measures. By showing how actors involved in AI development reflect on distribution of responsibility, this study contributes to a dialogue between the field of AI governance and the field of AI ethics. (shrink)

Epistocracy is rule by whoever is more likely to make correct decisions. AI epistocracy is rule by an artificial intelligence that is more likely to make correct decisions than any humans, individually or collectively. I argue that although various objections have been raised against epistocracy, the most popular do not apply to epistocracy organized around AI rule. I use this result to show that epistocracy is fundamentally flawed because none of its forms provide adequate opportunity for people (as opposed to (...) individuals) to develop a record of meaningful moral achievement. This Collective Moral Achievement Objection provides a novel reason to value democracy. It also provides guidance on how we ought to incorporate digital technologies into politics, regardless of how proficient these technologies may become at identifying correct decisions. (shrink)

Questions about robots feeling pain are important because the experience of pain implies sentience and the ability to suffer. Pain is not the same as nociception, a reflex response to an aversive stimulus. The experience of pain in others has to be inferred. Danaher’s (Sci Eng Ethics 26(4):2023–2049, 2020. https://doi.org/10.1007/s11948-019-00119-x) ‘ethical behaviourist’ account claims that if a robot behaves in the same way as an animal that is recognised to have moral status, then its moral status should also be assumed. (...) Similarly, under a precautionary approach (Sebo in Harvard Rev Philos 25:51–70, 2018. https://doi.org/10.5840/harvardreview20185913), entities from foetuses to plants and robots are given the benefit of the doubt and assumed to be sentient. However, there is a growing consensus about the scientific criteria used to indicate pain and the ability to suffer in animals (Birch in Anim Sentience, 2017. https://doi.org/10.51291/2377-7478.1200; Sneddon et al. in Anim Behav 97:201–212, 2014. https://doi.org/10.1016/j.anbehav.2014.09.007). These include the presence of a central nervous system, changed behaviour in response to pain, and the effects of analgesic pain relief. Few of these criteria are met by robots, and there are risks to assuming that they are sentient and capable of suffering pain. Since robots lack nervous systems and living bodies there is little reason to believe that future robots capable of feeling pain could (or should) be developed. (shrink)

This paper traces the shift from the age of realism to the age of virtualism we are currently witnessing. To do so, I draw on older theories announcing this advent (mostly Baudrillard in Simulacra and simulation. Transl. Sheila Glaser. University of Michigan, Ann Arbor, 1994 [1981]; Serres in Atlas. Édition Julliard, Paris, 1994; Virilio in The vision machine. Transl. Rose J. Indiana UP, Bloomington, 1994). I will describe how AI destabilizes fundamental distinctions upon which reality is built—such as the difference (...) between truth and fiction, between existence and simulation, between the actual and the potential, between game and everyday life, between person and avatar. Against the backdrop of a broader notion of the virtual (drawing on Deleuze in The actual and the virtual. Transl. Eliot Ross Albert in Parnet C Dialogues. Bloomsbury, London, pp 148–152, 2006 [1996]), I will read current realist theories (Chalmers in Reality plus—virtual worlds and the problems of philosophy. Allen Lane, London in 2022; Gabriel in Why the world does not exist. Transl. Gregory S. Moss. Polity, Cambridge, 2015; Ferraris in Positive realism. Zero Books, Arlesford, 2015; Meillassoux in After finitude—an essay on the necessity of contingency. Transl. Brassler R. Bloomsbury, London, 2008) as symptoms of the ongoing shift (rather than countermeasures against it), because they result in an inability to do justice to the epistemic and existential changes resulting from the erosion of the foundations of reality. The consequences of this shift are hard to predict and can only partially be outlined in this paper, which will end in a cautious attempt to do so (chapters 5 and 6). Starting off with a brief overview on this topic (chapter 1), I will trace the reasons for my diagnosis focusing first on what I call the “age of reality” currently coming to an end (chapter 2), then on why virtuality cannot be subsumed under reality (chapter 3), then how virtuality is currently replacing reality (chapter 4). (shrink)

This paper presents a framework for studying culture in the context of robot-robot interaction (RRI). We examine the claim that groups of robots can share a culture, even independently of their relationship with humans. At the centre of our framework is a recognition that ‘culture’ is a concept that can be defined and understood in many different ways. As we demonstrate, which definition of ‘culture’ one employs has important consequences for the question of whether groups of robots can have their (...) own culture, and what kind of culture they can have. We suggest that this argument has important consequences for robotics from an ethical/legal perspective. (shrink)

The primary aim of this paper is twofold: firstly, to argue that we can enter into relation of trust with robots and AI systems (automata); and secondly, to provide a comprehensive description of the underlying mechanisms responsible for this relation of trust. To achieve these objectives, the paper first undertakes a critical examination of the main arguments opposing the concept of a trust-based relation with automata. Showing that these arguments face significant challenges that render them untenable, it thereby prepares the (...) ground for the subsequent positive analysis, proposing a framework in which these challenges can be addressed. According to this framework trust does not originate from mere reliability, but rather from an empathic relation with automata. This initial empathic relation elevates the automata to the status of what I will term "Otheroids." The paper then explores how this human-Otheroid relationship inherently possesses the seeds for the development of trust. Finally, it examines how these seeds can grow into a basic form of trust with Otheroids through the establishment of a rich history of interaction. (shrink)

In Asian countries, there is a tradition that a rabbit, known as the Moon rabbit, lives on the Moon. Typically, two reasons are mentioned for the origin of this tradition. The first reason is that the color pattern of the lunar surface resembles the shape of a rabbit. The second reason is that both the Moon and rabbits are symbols of fertility, as the Moon appears and disappears (i.e., waxing and waning) cyclically and rabbits are known for their high fertility. (...) Considering the latter reason, is the color pattern of the lunar surface not similar to a rabbit? Here, the similarity between rabbit and the lunar surface pattern was evaluated using seven AI architectures. In the test conducted with Contrastive Language-Image Pre-Training (CLIP), which can classify images based on given words, it was assumed that people frequently observe the Moon in the early evening. Under this condition, the lunar surface pattern was found to be more similar to a rabbit than a face in low-latitude regions, while it could also be classified as a face as the latitude increases. This result is consistent with that the oldest literatures about the Moon rabbit were written in India and that a tradition of seeing a human face in the Moon exists in Europe. In a 1000-class test using seven AI architectures, ConvNeXt and CLIP sometimes classified the lunar surface pattern as a rabbit with relatively high probabilities. Cultures are generated by our attitude to the environment. Both dynamic and static similarities may be essential to induce our imagination. (shrink)

This text analyses how generative AI systems are being employed in current artistic practice to question certain historical visual narratives, creating representations that challenge some conventional perceptions of the past and thus opening up new perspectives on the experience of temporality. In this regard, special emphasis will be placed on some artistic projects based on generative historical photography practices. These are works that develop new ways around ‘archival aesthetics’ (Sekula in October 39:3–64 1986; Buchloh in Deep storage. collecting, storing and (...) archiving in art [Exhibition catalog]. P.S.1, Nueva York, 1999; Guasch in Arte y archivo, 1920-2010. Genealogías, tipologías y discontinuidades. Akal, Madrid, 2015, etc.) by producing visual archives that do not exist or are alternatives to others. We will therefore analyse works that critically examine how these generative systems can contribute to a revision and re-evaluation of the past, as well as to the problematisation of the ways in which photography has been used for the historical record. These poetics invite subtle reflections on the role of the visual archive in the processes of shaping subjectivity and personal and communal identity. In the final part of the text, we will deal with the study of some artists who, through appropriationist strategies and remakes assisted by generative AI models, revise artistic works from the past, specifically from the photoconceptualism of the 1970s. In these strategies, the thematisation of the relations between photographic register and temporality also plays a leading role. (shrink)

Meaningful human control (MHC) is increasingly becoming an important topic in AI ethics beyond the domain of autonomous weapons systems. MHC has been conceptualized, analyzed, and applied. However, in this article, I show how all the current attempts at realizing MHC have fallen short because we have not taken the important first step of deciding what machines should and should not be doing in the first place. We must first ensure that the output we have delegated to the machine is (...) appropriate – only then do we have to do the work required to ensure that MHC is realized. Here, I argue that machines should not be evaluating – that is, we should not be delegating evaluative outputs to machines. This is practically important because machines that have evaluative outputs cannot be evaluated for efficacy. We can’t say how effective they are. This is ethically important because machines don’t have the moral agency required to make evaluations. Furthermore, machines should not be in a position to change our values – which they would be if they were evaluating. Finally, evaluations are judged based on the considerations used to reach a particular judgment. Contemporary AI cannot provide these justifying considerations – so we have no way of evaluating their evaluative outputs. (shrink)

Deepfakes, as hyperrealistic digital fabrications, reveal gaps and uncertainties in existing ontological frameworks. Neither simply images nor realities, deepfakes occupy an ambiguous metaphysical position between concepts such as representation/simulation, human/machine, and real/artificial. Their emergent generation via AI and experiential traction as credible synthetic media underscores limitations in prevailing paradigms reliant on purified binaries and anthropocentric assumptions. Rather than anomalies, deepfakes epitomize the imperative for new ontological cartographies and conceptual vocabularies attuned to increasingly unbounded algorithmic creation. The paper surveys debates about (...) digital objects and theories of representation to contextualize this argument. Deepfakes’ ontological novelty stems from their ambiguous origins, autonomous proliferation, and infiltration of perceivable situations with credible fictions. This destabilizes ontological foundations such as creative intentionality and indexical truth. I conclude deepfakes signal the insufficiency of existing ontology for navigating realities transformed by generative technologies. New paradigms are needed that grapple with their radical metaphysical challenges. (shrink)

In this paper, we will survey the different uses of the term algorithm in contemporary legal practice. We will argue that the concept of algorithm currently exhibits a substantial degree of open texture, co-determined by the open texture of the concept of algorithm itself and by the open texture inherent to legal discourse. We will substantiate our argument by virtue of a case study, in which we analyze a recent jurisprudential case where the first and second-degree judges have carved-out contrasting (...) notions of algorithm. We will see that, thanks to our analysis of the open texture of the notion of algorithm in legal language, we can make sense of the different decisions taken by the judges as different contextually-determined sharpenings of the concept of algorithm. Finally, we will draw some general conclusions concerning the use of technical terms in legal instruments that address new technologies, such as the EU AI Act. (shrink)

In the research field of machine ethics, we commonly categorize artificial moral agents into four types, with the most advanced referred to as a full ethical agent, or sometimes a full-blown Artificial Moral Agent (AMA). This type has three main characteristics: autonomy, moral understanding and a certain level of consciousness, including intentional mental states, moral emotions such as compassion, the ability to praise and condemn, and a conscience. This paper aims to discuss various aspects of full-blown AMAs and presents the (...) following argument: the creation of full-blown artificial moral agents, endowed with intentional mental states and moral emotions, and trained to align with human values, does not, by itself, guarantee that these systems will have _human_ morality. Therefore, it is questionable whether they will be inclined to honor and follow what they perceive as incorrect moral values. we do not intend to claim that there is such a thing as a universally shared human morality, only that as there are different human communities holding different sets of moral values, the moral systems or values of the discussed artificial agents would be different from those held by human communities, for reasons we discuss in the paper. (shrink)

Complex technologies such as Artificial Intelligence (AI) can cause harm, raising the question of who is liable for the harm caused. Research has identified multiple liability gaps (i.e., unsatisfactory outcomes when applying existing liability rules) in legal frameworks. In this paper, the concepts of shared responsibilities and fiduciary duties are explored as avenues to address liability gaps. The development, deployment and use of complex technologies are not clearly distinguishable stages, as often suggested, but are processes of cooperation and co-creation. At (...) the intersections of these stages, shared responsibilities and fiduciary duties of multiple actors can be observed. Although none of the actors have complete control or a complete overview, many actors have some control or influence, and, therefore, responsibilities based on fault, prevention or benefit. Shared responsibilities and fiduciary duties can turn liability gaps into liability overlaps. These concepts could be implemented in tort and contract law by amending existing law (e.g., by assuming that all stakeholders are liable unless they can prove they did not owe a duty of care) and by creating more room for partial liability reflecting partial responsibilities (e.g., a responsibility to signal or identify an issue without a corresponding responsibility to solve that issue). This approach better aligns legal liabilities with responsibilities, increases legal certainty, and increases cooperation and understanding between actors, improving the quality and safety of technologies. However, it may not solve all liability gaps, may have chilling effects on innovation, and may require further detailing through case law. (shrink)

The integration of generative artificial intelligence (AI), particularly large language models like ChatGPT, presents new challenges as well as possibilities for scientific authorship. This paper draws on social systems theory to offer a nuanced understanding of the interplay between technology, individuals, society and scholarly authorial practices. This contrasts with orthodoxy, where individuals and technology are treated as essentialized entities. This approach offers a critique of the binary positions of sociotechnological determinism and accelerationist instrumentality while still acknowledging that generative AI presents (...) profound challenges to existing practices and meaning making in scientific scholarship. This holistic treatment of authorship, integrity, and technology involves comprehending the historical and evolutionary entanglement of scientific individuality, scientific practices, and meaning making with technological innovation. This addresses current needs for more robust theoretical approaches to address the challenges confronted by academicians, institutions, peer review, and publication processes. Our analysis aims to contribute to a more sophisticated discourse on the ethical and practical implications of AI in scientific research. (shrink)

Artificial Intelligence (AI) has the potential to influence people’s lives in various ways as it is increasingly integrated into important decision-making processes in key areas of society. While AI offers opportunities, it is also associated with risks. These risks have sparked debates about how AI should be regulated, whether through government regulation or industry self-regulation. AI-related risk perceptions can be shaped by national cultures, especially the cultural dimension of uncertainty avoidance. This raises the question of whether people in countries with (...) higher levels of uncertainty avoidance might have different preferences regarding AI regulation than those with lower levels of uncertainty avoidance. Therefore, using Hofstede’s uncertainty avoidance scale and data from ten European countries (N = 7.855), this study investigates the relationships between uncertainty avoidance, people’s AI risk perceptions, and their regulatory preferences. The findings show that people in countries with higher levels of uncertainty avoidance are more likely to perceive AI risks in terms of a lack of accountability and responsibility. While people’s perceived AI risk of a lack of accountability exclusively drives their preferences for government regulation of AI, the perceived AI risk of a lack of responsibility can foster people’s requests for government regulation and/or industry self-regulation. This study contributes to a better understanding of which mechanisms shape people’s preferences for AI regulation. (shrink)

The relevance of this study's topic is supported by the argument that a philosophical understanding of the fundamental concepts of epistemology as they pertain to the educational process is crucial as the educational setting becomes increasingly digitalised. This paper aims to explore the epistemological component of the philosophy of learning in light of the educational process digitalisation. The research comprised a sample of 462 university students from Kazakhstan, with 227 participants assigned to the experimental and 235 to the control groups. (...) The research employed a scientific methodology that encompassed various scientific methods, including sociological, socio-philosophical, and culturological analysis. In addition, philosophical methods such as system analysis, structural and functional analysis, and comparative and hermeneutical approaches were utilised for the philosophical and epistemological interpretation of the cognitive activity mechanism of students and its dynamics in light of digitalising education. Furthermore, psychological diagnostic methods such as SAMOAL personality self-actualisation diagnostics, personality volitional potential diagnostics, and psychodiagnostic methods were employed. This study offers a theoretical rationale for the integration of digital technology in the realm of education, grounded in philosophical and epistemological principles. In addition, it challenges the null hypothesis that the philosophical and epistemological framework of the educational process does not influence its cognitive and personal aspects. Digital learning has been found to improve both cognitive and personal aspects of the learning process. All indicators of self-realization increased statistically significantly (p 0.05). The positive impact of digital technologies on increasing the communicative competence of students can be considered as a factor in mitigating epistemic risks in the learning process.The findings can be used to avoid epistemological risks and improve learning effectiveness in a digital learning environment. There are plans to investigate the gender dimension of digital learning epistemology in the future. (shrink)