Alice C Helliwell

In recent years, there have been growing calls to operationalize artificial intelligence (AI) ethics - to move from theory to practice, or (as one group of authors has put it) ‘from what to how’ (Morley et al. 2020). In this paper, we propose a novel account of what ethical AI practice might look like, which we call the 3D model, named for its recognition, within the overall AI design cycle, of the three stages of design, development, and deployment. This model aims to embed ethics throughout this cycle, offering questions that should be addressed at each stage. We articulate the benefits of this approach to ethical AI practice: that it is pro-ethical and value-aware, amenable to implementation, it embeds ethics at every stage of the development process, it embeds a culture and language of ethics in organizations and provides clear decision points. Our model is not a panacea, of course, and we accordingly provide an indication of the context in which the implementation of our model might be most effective in ensuring ethical AI practice.

We can formulate an argument against AI creativity from agency. By some accounts, creativity requires agency, and agency is, many think, not possible for AI. This is due to the typical conception of agency as a capacity for intentional action. Intentional action is thought to require mental states, a severe challenge for machine intelligence. On the face of things, the agency argument seems to provide a straightforward route to argue for the impossibility of AI creativity. However, this path, I argue, is not so clear. In this paper, I outline the agency argument against AI creativity, before calling into question the apparent simplicity of this argument. I argue, ultimately, that the reasoning behind the inclusion of agency in accounts of creativity does not necessitate the use of intentional action, but can instead be satisfied by a minimal teleological account of agency.

Can Artificial Intelligence (AI) be creative? In order to address this question, we need to understand creativity before moving to apply our chosen definition to the assessment of AI. With this in mind, this article focuses on one component of one definition of creativity: flair. Flair is Berys Gaut’s addition to value and originality, the components typically included in accounts of creativity. Flair is the basis of Gaut’s rejection of AI creativity; but is he right to reject this possibility on these grounds? In this article I argue that flair is possible for AI. I begin by outlining the flair condition. I then turn to assess whether flair is possible for AI, arguing that it is potentially achievable. I will then consider Gaut’s objection to the possibility of AI creativity through flair. I argue that if we are happy to accept that skill is sufficient for flair in place of understanding, then flair is possible for AI. However, there remain two issues, which I will then turn to examine. The first issue is to clarify the relationship between skill and understanding in order to establish whether they can indeed be pulled apart in flair. The second issue is to consider whether flair is inextricably linked to agency, and thus is not possible for AI. I conclude that flair is possible for AI systems to achieve if we do not require understanding, suggesting that we can instead rely upon skill to help delineate creative processes in non-agents.

This paper explores what computational methodologies can tell us about philosophical education, particularly in the context of artificial intelligence (AI) ethics. Taking the readings on our AI ethics and responsible AI syllabi as a corpus of AI ethics literature, we conduct an analysis of the content of these courses through a variety of methods: word frequency analysis, term frequency–inverse document frequency (TF–IDF) scoring, document vectorization via SciBERT, clustering via k-means, and topic modelling using latent Dirichlet allocation (LDA). We reflect on the findings of these analyses, and more broadly on what computational approaches can offer to the practice of philosophical education. Finally, we compare our approach to previous computational approaches in philosophy, and more broadly in the digital humanities. This project offers a proof of concept for how contemporary natural-language processing techniques can be used to support philosophical pedagogy: not only to reflect critically on what we teach, but to discover new materials, explore conceptual gaps, and make our courses more accessible to students from a range of disciplinary backgrounds.

In 2022, Stephen Marche, writing for The Atlantic, claimed, “We’re Witnessing the Birth of a New Artistic Medium.” However, since then, we have seen dismissive criticisms of the use of AI in the arts. This paper takes Marche’s claim seriously and argues that Artificial Intelligence (AI), understood as machine learning algorithms, is a new artistic medium. In this paper, I begin by discussing the concept of the medium in the arts. I apply this concept to AI, following the tradition of legitimisation and evaluation of new media through medium analysis (as has been done with photography, cinema, comics, and video games). I also consider some initial objections about AI as an artistic medium, arguing that these objections fail. I then move on to discuss the medium-specificity thesis. This is the idea that each medium of art offers particular qualities that affect what artists can achieve within it, and this, in turn, informs how we can properly evaluate works in that medium. I apply medium specificity to AI, considering first a strong version of medium specificity, then Gaut’s weaker (but arguably more defensible) claims of medium specificity. Through this, I argue that AI can be considered as a medium with unique features, the quality of which we might call ‘machinic’ (much like the cinematic as applied to the medium of cinema). Finally, I argue that AI may indeed constitute not just a medium but also an artform, albeit one which we have yet to see reach full potential. With this paper, I aim to demonstrate that understanding AI as an artistic medium is helpful for reasoning about works of art made with AI, and suggest a potential way forward for critical evaluation of AI art.

Embodied semi/autonomous systems (e.g., robots) have tremendous potential to improve the human experience, especially if they can be developed from tools and/or semi/autonomous interactive agents to become true collaborative teammates. But what exactly is required to achieve such autonomy in human-robot teams? Our interdisciplinary investigation in this paper is both empirical and conceptual. We argue that autonomous teams require an interdependence between teammates sharing an ultimate goal, and able to flexibly adopt and embrace intermediate goals in a manner that (empirically) requires both (social) intelligence (aka theory of mind) and (a limited form of) authenticity. This is (conceptually) compatible with robotic team members being autonomous only in an attenuated sense that bears a certain family resemblance to autonomy in humans.

Is AI art really art? This question has been the subject of much public discussion and is one that philosophical aesthetics should be well-placed to address. Unfortunately, there is no clear consensus within the discipline on how to tackle key definitional questions such as this. In the case of AI, we can add to this the unique challenge of works not made by humans. In this chapter, I argue for the utility of a Wittgensteinian approach to the question of whether AI art is art. This typically repudiates the need to provide necessary and sufficient conditions, when addressing the topic of AI art. Using Gaut’s cluster account, I show that AI art can indeed count as art. I also demonstrate that the cluster account of art is particularly useful for thinking about art made by AI.

The two volumes on Wittgenstein and AI aim to trace and suggest Wittgensteinian influences in some of the most cutting-edge areas of research in Artificial Intelligence (such as Computation, Machine Learning, Natural Language Processing and the use of automation in legal settings). The collection is driven by an essentially interdisciplinary approach, featuring articles from philosophers, computer scientists and legal scholars, writing on a broad range of topics in AI. The chapters across these two volumes are grouped into two sets of themes: Mind and Language and Value and Governance. These themes correspond to two major areas of research in the philosophical study of AI: the computational mind and the ethics of artificial intelligence. These volumes make a significant and unprecedented contribution to the question of what Wittgenstein’s philosophy can offer to the ever-growing field of AI. It aims to provide insight for both philosophers and non-philosophers alike, offering reflection on the significance of Wittgenstein’s work for AI, and on the implications of advancements in AI technology for Wittgenstein’s philosophy and philosophy influenced by Wittgenstein. VOLUME II: Wittgenstein and AI (Volume II): Value and Governance. This volume includes chapters on ethical AI, rules in AI, rules and the law, human-AI interaction, the moral implications of robotics and the status of AI art.

We can formulate an argument against AI creativity from agency. By some accounts, creativity requires agency, and agency is, many think, not possible for AI. This is due to the typical conception of agency as a capacity for intentional action. Intentional action is thought to require mental states, a severe challenge for machine intelligence. On the face of things, the agency argument seems to provide a straightforward route to argue for the impossibility of AI creativity. However, this path, I argue, is not so clear. In this paper, I outline the agency argument against AI creativity, before calling into question the apparent simplicity of this argument. I argue, ultimately, that the reasoning behind the inclusion of agency in accounts of creativity does not necessitate the use of intentional action, but can instead be satisfied by a minimal teleological account of agency.

The threat from possible future superintelligent AI has given rise to discussion of the so-called “value alignment problem”. This is the problem of how to ensure artificially intelligent systems align with human values, and thus (hopefully) mitigate risks associated with them. Naturally, AI value alignment is often discussed in relation to morally relevant values, such as the value of human lives or human wellbeing. However, solutions to the value alignment problem target all human values, not only morally relevant ones. Is there a value alignment problem in other domains? In this paper, I explore whether the AI value alignment problem extends beyond morally relevant values to include aesthetic values. I demonstrate that the value alignment problem as typically framed includes aesthetic values, and, using examples from computer vision, put forward that AI may be misaligned with human values in the artistic realm. Whilst misalignment may be cause for concern when considering AI creativity, I argue that aesthetic value is a case in which we do not want AI to be fully aligned with human values. In doing so, I offer support to Peterson’s moderate value alignment thesis.

This philosophical paper examines the Darwinian account of creativity as a model for assessing computational creativity. It will first establish a Darwinian account of creativity using Simonton’s model. It will then apply this model to popular image-producing AI, Generative Adversarial Networks, and the promising Creative Adversarial Network, both used in the computational production of ‘artworks’. The paper will argue that these networks are compatible with a Darwinian account of creativity, due to the presence of blind variation within the networks, a key component of Simonton’s model. The paper will then address some initial objections. The aim of this paper will ultimately be to assess whether the AI systems are compatible with the Darwinian model of creativity, and in the process explore Darwinian creativity as a potential standard for testing computational creativity.

Andy Clark and David Chalmers’s theory of extended mind can be reevaluated in today’s world to include computational and Artificial Intelligence (AI) technology. This paper argues that AI can be an extension of human mind, and that if we agree that AI can have mind, it too can be extended. It goes on to explore the example of Ganbreeder, an image-making AI which utilizes human input to direct behavior. Ganbreeder represents one way in which AI extended mind could be achieved. The argument of this paper is that AI can utilize human input as a social extension of mind, allowing AI to access the external world that it would not otherwise be able to access.