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This article investigates how human interactions with AI-powered chatbots may offend human dignity. Current chatbots, driven by large language models, mimic human linguistic behaviour but lack the moral and rational capacities essential for genuine interpersonal respect. Human beings are prone to anthropomorphize chatbots – indeed, chatbots appear to be deliberately designed to elicit that response. As a result, human beings' behaviour towards chatbots often resembles behaviours typical of interaction between moral agents. Drawing on a second-personal, relational account of dignity, we argue that interacting with chatbots in this way is incompatible with the dignity of users. We show that, since second-personal respect is premised on reciprocal recognition of second-personal moral authority, behaving towards chatbots in ways that convey second-personal respect is bound to misfire in morally problematic ways, given the lack of reciprocity. Consequently, such chatbot interactions amount to subtle but significant violations of self-respect – the respect we are duty-bound to show for our own dignity. We illustrate this by discussing four actual chatbot use cases (information retrieval, customer service, advising, and companionship), and propound that the increasing societal pressure to engage in such interactions with chatbots poses a hitherto underappreciated threat to human dignity.

There is broad consensus that disciplinary higher education must include artificial intelligence (AI) content tailored to the needs of different domains. Such an approach to higher education will help cultivate the skills, knowledge and critical thinking necessary for the socially beneficial development and integration of AI into the routines, organisations and infrastructures that shape society. Such an approach to higher education further aligns with broader policy and legal recommendations and requirements for upskilling, reskilling, AI literacy and interdisciplinary education to enable individuals to live with, work with, contribute to developing, and navigate AI technologies.

The paper provides insights on how an empirical approach to the pressing question of educating AI broadly in higher education can be rewardingly pursued. We focus on the findings from an interdisciplinary small-scale qualitative study. The study is based on a workshop conducted in 2024 in Uppsala University within the WASP-ED programme – a nation-wide educational and interdisciplinary programme aimed at increasing the capability and capacity of Swedish universities in providing relevant and scalable education in AI.

The aim of the workshop was to lay the ground for the development of course syllabi on AI -focused curricula across disciplines and academic institutions in Swedish higher education. 13 researchers across nine academic institutions participated in the workshop. The researchers, primarily from the social sciences and humanities, are all engaged in interdisciplinary scholarship related to AI.

The paper presents the theoretical grounding of the workshop and its methodological design, and outlines the analytical steps taken to identify major pedagogical principles and topics in AI that could be developed and integrated into educational curricula. The results outline five themes that emerged during the workshop: AI, diversity and human rights; Educating on the foundations of AI; Pushing the boundaries of theories and methodology; Building better AI systems; and Educating professionals within certain disciplines – changing professions.

We compiled the themes into a blueprint of an advanced level 60-credit programme, which could be developed into a 120-credit master’s programme. The programme consists of four modules: Foundations of AI in Society; AI in the Wild; Pushing Boundaries Building better AI; and Thesis. The participants agreed on three pedagogical principles to align the programme content and courses with societal needs: Learning by doing; Real world challenges; Engaging stakeholders. We evaluated the blueprint by mapping it to the WASP-ED AI Curriculum and identified different opportunities to extend the blueprint into a master’s programme.

We suggest that the blueprint can be used by educational institutions to create courses and programmes aimed at developing disciplinary education as well as fostering multidisciplinary understanding of AI in society. Furthemore, we suggest that the methods through which the blueprint was developed offer valuable insights on how interdisciplinary education on AI can be productively developed.

Research on the nature and varieties of the format of cognitive representations in philosophy and cognitive science has been partly shaped by analogies to external, public representations. In this chapter, we argue that relying on such analogies contributes to framing the question of cognitive formats in problematic, potentially counterproductive ways. We show that cognitive and public representations differ in many of their central features, making analogies to public representations ill-suited to improving our understanding of cognitive formats. We illustrate these points by examining two case studies in which analogies to public representations may have had a negative impact on research: the 1980s to 1990s debate about compositionality and cognitive architecture between symbolicists and connectionists and contemporary discussions about the nature of visual demonstratives. Finally, we outline an alternative, computational account of formats that does not share the shortcomings of appeal to public representations.

Large language models (LLMs) produce seemingly meaningful outputs, yet they are trained on text alone without direct interaction with the world. This leads to a modern variant of the classical symbol grounding problem in AI: can LLMs' internal states and outputs be about extra-linguistic reality, independently of the meaning human interpreters project onto them? We argue that they can. We first distinguish referential grounding—the connection between a representation and its worldly referent—from other forms of grounding and argue it is the only kind essential to solving the problem. We contend that referential grounding is achieved when a system's internal states satisfy two conditions derived from teleosemantic theories of representation: (1) they stand in appropriate causal-informational relations to the world, and (2) they have a history of selection that has endowed them with the function of carrying this information. We argue that LLMs can meet both conditions, even without multimodality or embodiment.

Artificial Intelligence is a field that lives many lives, and the term has come to encompass a motley collection of scientific and commercial endeavours. In this paper, I articulate the contours of a rather neglected but central scientific role that AI has to play, which I dub “AI-as-exploration”. The basic thrust of AI-as-explora tion is that of creating and studying systems that can reveal candidate building blocks of intelligence that may dif fer from the kinds of human and animal intelligence we are familiar with. In other words, I suggest that AI is one of the best tools we have for exploring intelligence space, namely the space of possible intelligent systems. I illus trate the value of AI-as-exploration by focusing on a specific case study, i. e., recent work on the capacity to com bine novel and invented concepts in humans and Large Language Models. I show that the latter, despite showing human-level accuracy in such a task, probably solve it in ways radically different, but no less relevant to intelli gence research, to those hypothesised for humans.

La inteligencia artificial es un campo con múltiples vidas, y el término ha llegado a abarcar un conjunto heterogéneo de iniciativas científicas y comerciales . En este artículo, articulo los contornos de un pa-pel científico, bastante descuidado pero central, que la IA debe desempeñar, al que denomino «IA como explo-ración» . El objetivo fundamental de la IA como exploración es crear y estudiar sistemas que puedan revelar posi-bles bloques de construcción de inteligencia, que pueden diferir de los tipos de inteligencia humana y animal con los que estamos familiarizados . En otras palabras, sugiero que la IA es una de las mejores herramientas que tene-mos para explorar el espacio de la inteligencia, es decir, el espacio de posibles sistemas inteligentes . Ilustro el valor de la IA como exploración centrándome en un caso práctico específico: trabajos recientes sobre la capacidad de combinar conceptos inventados y novedosos en humanos y los Modelos de Lenguaje Extenso (Large Language Models) . Demuestro que estos últimos, a pesar de mostrar una precisión a nivel humano en dicha tarea, proba-blemente la resuelvan de maneras radicalmente diferentes, pero no menos relevantes para la investigación de la in-teligencia, que las hipotetizadas para humanos .

This paper critically evaluates the attempts to align Artificial Intelligence (AI) systems, especially Large Language Models (LLMs), with human values and intentions through Reinforcement Learning from Feedback methods, involving either human feedback (RLHF) or AI feedback (RLAIF). Specifically, we show the shortcomings of the broadly pursued alignment goals of honesty, harmlessness, and helpfulness. Through a multidisciplinary sociotechnical critique, we examine both the theoretical underpinnings and practical implementations of RLHF techniques, revealing significant limitations in their approach to capturing the complexities of human ethics, and contributing to AI safety. We highlight tensions inherent in the goals of RLHF, as captured in the HHH principle (helpful, harmless and honest). In addition, we discuss ethically-relevant issues that tend to be neglected in discussions about alignment and RLHF, among which the trade-offs between user-friendliness and deception, flexibility and interpretability, and system safety. We offer an alternative vision for AI safety and ethics which positions RLHF approaches within a broader context of comprehensive design across institutions, processes and technological systems, and suggest the establishment of AI safety as a sociotechnical discipline that is open to the normative and political dimensions of artificial intelligence.

Crane (2024) argues against what he calls the ‘aboutness assumption’, which he takes to be the basic position in contemporary philosophy of mind. According to this assumption, in order to solve the problem of aboutness we must find a single account that explains how the various types of representation (external and internal) come to be about states and events in the world. For Crane, the aboutness assumption is underpinned by the idea that there is an essential similarity between external and internal representations when it comes to their aboutness—based, in its turn, on strong, debatable commitments, for example, to physicalism about the mental.

I present two lines of criticism of Crane’s analysis. First, I show that many influential philosophical theories of mind and cognition do not subscribe to the aboutness assumption. Second, I argue that the idea that there is an essential similarity between external and internal representations does not hinge on strong philosophical pre-commitments, but rather on the extension of a successful model of explanation of complex behaviour based on external representations to the mental, internal case. This realisation, moreover, opens up considerable space for the kind of pluralism about intentionality that Crane advocates.

The notion of intelligence is relevant to several fields of research, including cognitive and comparative psychology, neuroscience, artificial intelligence, and philosophy, among others. However, there is little agreement within and across these fields on how to characterise and explain intelligence. I put forward a behavioural, operational characterisation of intelligence that can play an integrative role in the sciences of intelligence, as well as preserve the distinctive explanatory value of the notion, setting it apart from the related concepts of cognition and rationality. Finally, I examine a popular hypothesis about the underpinnings of intelligence: the capacity to manipulate internal representations of the environment. I argue that the hypothesis needs refinement, and that so refined, it applies only to some forms of intelligence.

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a variety of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence.