Steven Gouveia

This Handbook explores the ethical dimensions of Artificial Intelligence (AI) across philosophical, legal, and applied perspectives. Bringing together leading scholars and practitioners, the handbook examines key issues in this field, including algorithmic bias, privacy, responsibility, and AI’s impact on human rights and democracy. It also explores sector-specific challenges in healthcare, law, finance, art and many more, offering a multidisciplinary approach to AI Ethics. With a balance of theoretical insights and practical studies and with a diversity of authorship, this handbook serves as an essential resource for academics, policymakers, and technology professionals navigating the ethical challenges of AI in the 21st century.

In this paper, I want to briefly introduce how four approaches that relate (or not) philosophical investigation with the neuroscientific research can be considered in order to achieve a methodology that can effectively investigate the phenomenon of the conscious mind and its relationship with the brain. The traditional way of defining philosophical work seems to be clearly opposed to the traditional way of conceiving neuroscientific work. That raises the question that serves as the title of the paper: should we conceive the relationship between philosophy and neuroscience as a kind of “solo dancing”, that is, isolated without any kind of interaction? Or should we conceive that relationship as a “tango”, that is, an actively continuous relationship that complements each other? The Isolationist Approach argues for a “solo dancing” relationship, denying any kind of interaction. Next, rejecting this “solo dancing” approach, the Reductionist Approach (RA) will focus on specifying how philosophical work can be reduced to neuroscientific work. Arguing against the reductive aspect of (RA), but maintain a “tango” relationship, the Neurophenomenological Approach (NA) will demonstrate the importance of an embodied approach to the study of the conscious mind. Finally, the Non-Reductive Neurophilosophical Approach (NRNA) will expose a particular methodology that conceives the epistemic utility of both philosophical and neuroscientific work to investigate the conscious brain. After this brief reflection, we will provide some experimental philosophy data collected on how experts in neurophilosophy (broadly considered) judge this relationship, showing that there is a preference for approaches that interconnect both disciplines (even if the specific kind of interconnections is not particular).

This paper examines whether contemporary generative artificial intelligence (GAI), especially large language models (LLMs), can be regarded as a “cognitive subject” in the epistemic sense relevant to the production and endorsement of knowledge claims. GAI systems increasingly participate in writing, research, and decision-making workflows and can display striking competence in information processing and task-directed problem solving. Yet, the thesis that GAI is a cognitive subject is stronger than the observation that GAI contributes as a cognitive tool. Therefore, we propose an explicit set of necessary and sufficient conditions for cognitive subjecthood and evaluate each condition in light of recent philosophical and empirical scholarship. The analysis supports a two-part conclusion: (i) present-day GAI can reasonably be described as a cognitively significant contributor to knowledge production, but (ii) it does not satisfy the conditions for cognitive subjecthood, largely because robust intentionality, metacognitive self-representation, and consciousness-related indicator properties are not established.

The application of AI in medicine (AIM) is producing health practices more reliable, accurate and efficient than traditional medicine (TM) by assisting partly / totally the medical decision-making, such as the use of deep learning in diagnostic imagery, designing treatment plans or preliminary diagnosis. Yet, most of these AI systems are pure “black-boxes”: the practitioner understands the inputs and outputs of the system but cannot have access to what happens “inside” it and cannot offer an explanation, creating an opaque process that culminates in a “trust gap” in two levels: (a) between patients and the medical experts; (b) between the medical expert and the medical process itself. This creates a “black-box medicine” since the practitioner ought to rely (epistemically) on these AI systems that are more accurate, fast and efficient than any expert or group of human experts but are not transparent (epistemically) and do not offer any kind of explanation. In this paper, we want to introduce some preliminary remarks about the pros and cons of three possible solutions to deal with the “trust gap” in AIM.

In this paper, we argue that one way to approach what is known in the literature as the “Trust Gap” in Medical AI is to focus on explanations from an Explainable AI (xAI) perspective. Against the current framework on xAI – which does not offer a real solution – we argue for a pragmatist turn, one that focuses on understanding how we provide explanations in Traditional Medicine (TM), composed by human agents only. Following this, explanations have two specific relevant components: they are usually (i) social and (ii) abductive. Explanations, in this sense, ought to provide understanding by answering contrastive _why-questions_: “Why had P happened instead of Q?” (Miller in AI 267:1–38, 2019) (Sect. 1 ). In order to test the relevancy of this concept of explanation in medical xAI, we offer several reasons to argue that abductions are crucial for medical reasoning and provide a crucial tool to deal with trust gaps between human agents (Sect. 2 ). If abductions are relevant in TM, we can test the capability of Artificial Intelligence systems on this merit. Therefore, we provide an analysis of the capacity for social and abductive reasoning of different AI technologies. Accordingly, we posit that Large Language Models (LLMs) and transformer architectures exhibit a noteworthy potential for effective engagement in abductive reasoning. By leveraging the potential abductive capabilities of LLMs and transformers, we anticipate a paradigm shift in the integration of explanations within AI systems. This, in turn, has the potential to enhance the trustworthiness of AI-driven medical decisions, bridging the Trust Gap that has been a prominent challenge in the field of Medical AI (Sect. 3 ). This development holds the potential to not only improve the interpretability of AI-generated medical insights but also to guarantee that trust among practitioners, patients, and stakeholders in the healthcare domain is still present.

Does artificial intelligence (AI) exhibit consciousness or self? While this question is hotly debated, here we take a slightly different stance by focusing on those features that make possible both, namely a basic or fundamental subjectivity. Learning from humans and their brain, we first ask what we mean by subjectivity. Subjectivity is manifest in the perspectiveness and mineness of our experience which, ontologically, can be traced to a point of view. Adopting a non-reductive neurophilosophical strategy, we assume that the point of view exhibits two layers, a most basic neuroecological and higher order mental layer. The neuroecological layer of the point of view is mediated by the timescales of world and brain, as further evidenced by empirical data on our sense of self. Are there corresponding timescales shared with the world in AI and is there a point of view with perspectiveness and mineness? Discussing current neuroscientific evidence, we deny that current AI exhibits a point of view, let alone perspectiveness and mineness. We therefore conclude that, as per current state, AI does not exhibit a basic or fundamental subjectivity and henceforth no consciousness or self is possible in models such as ChatGPT and similar technologies.

With worldwide spending estimates of over $97 billion by 2023, it is no surprise that Artificial Intelligence is one of the hottest topics at present in both the private and public spheres. Comprising of vital contributions from the most influential researchers in the field, including Daniel Dennett, Roman V. Yampolskiy, Frederic Gilbert, Stevan Harnad, David Pearce, Natasha Vita-More, Vernon Vinge and Ben Goertzel, 'The Age of Artificial Intelligence: An Exploration' discusses a variety of topics ranging from the various ethical issues associated with A.I. based technologies in terms of morality and law to subjects related to artificial consciousness, artistic creativity and intelligence. The volume is organized as follows: Section I is dedicated to reflections on the Intelligence of A.I., with chapters by Soenke Ziesche and Roman V. Yampolskiy, Stevan Harnad, Daniel Dennett and David Pearce. Next, Section II discusses the relationship between consciousness, simulation and artificial intelligence, with chapters by Gabriel Axel Montes and Ben Goertzel, Cody Turner, Nicole Hall and Steven S. Gouveia. Section III, dedicated to aesthetical creativity and language in artificial intelligence, includes chapters by Caterina Moruzzi, René Mogensen, Mariana Chinellato Ferreira and Kulvinder Panesar. The subsequent Section IV is on the Ethics of the Bionic Brain with the participation of Peter A. DePergola II, Tomislav Miletic and Frederic Gilbert, Aníbal M. Astobiza, Txetxu Ausin, Ricardo M. Ferrer and Stephen Rainey and Natasha Vita-More. Finally, Section V follows on the Ethics of Artificial Intelligence with chapters by Federico Pistono and Roman V. Yamploskiy, Hasse Hämäläinen, Vernon Vinge and Eray Özkural. The Age of Artificial Intelligence is imminent, if not here already. We should ensure that we invest in the right people and the right ideas to create the best possible solutions to the problems of the present and prepare for those of the future. This edited volume will be of particular interest to researchers in the field of A.I. as well of those in Cognitive Science, Aesthetics and Arts, Applied Ethics and Political Philosophy / Law. Students studying the aforementioned topics can also benefit from its contents.