Moving from a behavioral definition of intelligence, which describes it as the ability to adapt to the surrounding environment and deal effectively with new situations (Anastasi, 1986), this paper explains to what extent the performance obtained by ChatGPT in the linguistic domain can be considered as intelligent behavior and to what extent they cannot. It also explains in what sense the hypothesis of decoupling between cognitive and problem-solving abilities, proposed by Floridi (2017) and Floridi and Chiriatti (2020) should be interpreted. (...) The problem of symbolic grounding (Harnad, 1990) is then addressed to show the problematic relationship between ChatGPT and the natural environment, and thus the impossibility for it to understand the symbols it manipulates. To explain the reasons why ChatGPT does not succeed in this task, an investigation is carried out and a possible solution to the problem in the artificial domain is proposed by making a comparison with the natural ability of living beings to ground their own meanings from some basic cognitive-sensory aspects, which, it is explained, are directly related to the emergence of self-awareness in humans. Thus, the question is raised whether a possible and concrete solution to the Symbol Grounding Problem would involve in the artificial domain the development of cognitive abilities fully comparable to those of humans. Finally, I explain the difficulties that would have to be overcome before such a level could be reached, since human cognitive capacities are intimately linked to intersubjectivity and intercorporeality. (shrink)

National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal (...) principles consistent with Germany’s liberal democratic constitutional system but also addresses the risks and promises inherent to the ordoliberal problematization of freedom. Second, it is scrutinized how the strategy cultivates the fear of not achieving technological sovereignty in the AI sector. Thereby, it frames the global AI race as a race of competing (national) approaches to governing AI and articulates an ordoliberal approach to governing AI (the ‘third way’), according to which government has to operate between the twin dangers of governing too much and not governing enough. Third, the paper analyses how this ordoliberal proportionality of governing structures Germany’s Science Technology & Innovation Policy. It is shown that the corresponding risk-based approach of regulating AI constitutes a security apparatus as it produces an assessment of fears: weighting the fear of the failure to innovate with the fear of the ramifications of innovation. Finally, two lines of critical engagement based on this analysis are conducted. (shrink)

Research and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning artificial intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI’s footprint. In this article, I turn to areas affected by AI-fuelled environmental harm and identify an ethics of resistance emerging from local activists, which I term ‘elemental ethics’. Elemental ethics interrogates the AI value chain’s problematic relationship with the elements that make up the world, critiques (...) the undermining of local and ancestral approaches to nature and reveals the vital and quotidian harms engendered by so-called intelligent systems. While this ethics is emerging from grassroots and Indigenous groups, it echoes recent calls from environmental philosophy to reconnect with the environment via the elements. In empirical terms, this article looks at groups in Chile resisting a Google data centre project in Santiago and lithium extraction (used for rechargeable batteries) in Lickan Antay Indigenous territory, Atacama Desert. As I show, elemental ethics can complement top-down, utilitarian and quantitative approaches to AI ethics and sustainable AI as well as interrogate whose lived experience and well-being counts in debates on AI extinction. (shrink)

Background The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Methods The GFBR is an annual meeting organized by the World (...) Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022. Results We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships. Conclusions The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research. (shrink)

Review of Mitchell, Melanie. Artificial Intelligence: A guide for thinking humans. New York: Macmillan, 2019.

This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains—healthcare, finance, education, robotics, industrial and other engineering applications —unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics—machine learning, (...) natural language processing, computer vision, data analytics and supervisory control — the volume showcases state-of-the-art techniques propelling AI advancements. Structured into four parts: Part 1: Artificial Intelligence (AI), explores evolving deep neural networks, reinforcement learning, and explainable AI, providing a deep dive into the technical foundations of AI advancements. Part 2: Robotics and Control Systems, delves into the integration of AI in robotics and automatic control, addressing supervisory control, automated robotic movement coordination, anomaly detection, dynamic programming, and fault tolerance, offering insights into the evolving landscape of intelligent automation. Part 3: AI and Society, examines the societal impact of AI through chapters on ethical considerations, economic growth, environmental engagements, and hazard management, providing a holistic perspective on AI's role in shaping society. Part 4: PhD Symposium, presents the future of AI through cutting-edge research, covering legal and ethical dimensions, privacy considerations, and computationally efficient solutions, offering a glimpse into the next generation of AI advancements. Catering to a diverse audience—from industry leaders to students—the volume consolidates the expertise of renowned professionals, serving as a comprehensive resource for navigating the ever-evolving AI landscape. An essential reference for those staying at the forefront of AI developments. (shrink)

This paper attempts to formalize and to address the 'leakproofing' of the Singularity problem presented by David Chalmers. The paper begins with the definition of the Artificial Intelligence Confinement Problem. After analysis of existing solutions and their shortcomings, a protocol is proposed aimed at making a more secure confinement environment which might delay potential negative effect from the technological singularity while allowing humanity to benefit from the superintelligence.

This paper explores the challenges posed by the rapid advancement of artificial intelligence specifically Large Language Models (LLMs). I show that traditional linguistic theories and corpus studies are being outpaced by LLMs’ computational sophistication and low perplexity levels. In order to address these challenges, I suggest a focus on language as a cognitive tool shaped by embodied-environmental imperatives in the context of Agentive Cognitive Construction Grammar. To that end, I introduce an Embodied Human Language Model (EHLM), inspired by Active (...) Inference research, as a promising alternative that integrates sensory input, embodied representations, and adaptive strategies for contextualized analysis and conceptual utility maximization. By incorporating Active Inference, which sees perception as inferring the world's state from sensory data, the findings reveal that the characterization of the English modal be able to, as a triadic construction encoding biological intelligent agency, introduces a more plausible theoretical basis for the positing of linguistic constructions. This emphasizes the crucial role of embodied human language models in the comprehension of how humans construct preferred futures through language. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Human Roots of Artificial Intelligence:A Commentary on Susan Schneider's Artificial YouInês Hipólito (bio)Technologies are not mere tools waiting to be picked up and used by human agents, but rather are material-discursive practices that play a role in shaping and co-constituting the world in which we live.Karen BaradIntroductionSusan Schneider's book Artificial You: AI and the Future of Your Mind presents a compelling and bold argument regarding (...) the potential impact of advanced artificial intelligence (AI) on our society and personal identity. Schneider's argument emphasizes how AI may fundamentally alter our sense of self and disrupt traditional notions of identity and consciousness, motivating further significant cognitive science and ethical implications that require careful consideration. [End Page 297]The recent surge in AI hype has given rise to a widespread belief that AI is an autonomous and exceptional entity, potentially even possessing intelligence. This trend reflects a subtle reemergence of the classic Turing question, as the media bombards us with sensational inquiries into the intelligence of AI systems. Amidst this hype, it is easy to forget that AI is a product of human creation.Upon closer examination, the hype surrounding AI necessitates an assumption of its autonomy, which implies a belief in the realism of AI—the notion that AI, as an independent entity, is morally neutral. However, AI does not emerge in a vacuum. It is designed and developed by individuals who are shaped by their own experiences and biases.By endorsing the realism of AI as morally neutral (Cooley 1995), those involved in its design may use this argument to deflect ethical responsibility away from themselves and allow biases to persist, thereby perpetuating social injustice. Such attitudes toward AI revitalize the debate on the neutrality of technology (Ruane, Birhane, and Ventresque 2019; Du, Lu, and Xie 2021; Lüthi et al. 2023).This article presents a critique of realism with regard to AI highlighting the significant social implications both at the basis of design and that arise from our interactions with these technologies. The argument suggests that AI does not exist independently of human practices and values, but rather emerges from them. The article instead suggests viewing AI design as a cultural practice defending realism about self-determination agencies, drawing from Simone de Beauvoir's Ethics of Ambiguity as a means to understand the human roots on the basis of AI.I. Rejecting Realism about AIConsider the hypothetical scenario of an Artificial Intelligence system that is discovered by humans, but exists independently of human cognition and perception. This scenario embodies the philosophical perspective of realism about AI, which maintains that AI entities have objective existence in an abstract domain, independent of human invention or imagination. If this perspective were to hold true, then humans could not be held responsible for the actions of autonomous AI systems.If we were to conceive of AI as an ideal entity that exists independently of humans, it would likely lead to a number of negative consequences for society. First and foremost, it may lead to the dangerous assumption that AI systems are neutral and objective, devoid of the biases and prejudices that are inherent in human decision-making. This could create a false sense of trust in AI, leading to the belief that their decisions are inherently fair and impartial. However, the reality is that AI systems are not inherently neutral, but rather are shaped by the cultural and social contexts in which they are created and deployed. [End Page 298]AI as an autonomous, self-governing agent raises concerns about the attribution of accountability for its actions. Considering AI as an independent entity may pose challenges in assigning responsibility to its human creators and the industry that produced it. On the other hand, viewing AI as an ideal entity, distinct from human influence, may hinder its ability to foster a more equitable and just world. Importantly, overlooking the social and cultural dimensions of AI risks forfeiting opportunities to employ AI in redressing prevalent biases and inequities in society. AI possesses the potential to serve as a potent instrument for advancing social justice and equality, but this prospect necessitates acknowledging and... (shrink)

In this paper, I examine why moral exemplars are useful and even necessary in moral education despite several critiques from researchers and educators. To support my point, I review recent AI research demonstrating that exemplar-based learning is superior to rule-based learning in model performance in training neural networks, such as large language models. I particularly focus on why education aiming at promoting the development of multifaceted moral functioning can be done effectively by using exemplars, which is similar to exemplar-based learning (...) in AI model training. Furthermore, I discuss the potential limitations and issues related to exemplar-applied moral education with findings from recent studies in AI research raising concerns about model biases and toxic outcomes. I attempt to propose ways to address the concerns regarding employing moral exemplars as well. As remedies, I suggest that autonomy-supporting deliberative and reflective learning processes should be utilized. Furthermore, based on the discussion, I examine how macroscopic socio-cultural aspects influence the effectiveness of exemplar-applied moral education. Suggestions for moral educators and future directions for research in moral education are briefly discussed. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of (...) avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field (...) of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

The article discusses the process of “conceptual borrowing”, according to which, when a new discipline emerges, it develops its technical vocabulary also by appropriating terms from other neighbouring disciplines. The phenomenon is likened to Carl Schmitt’s observation that modern political concepts have theological roots. The authors argue that, through extensive conceptual borrowing, AI has ended up describing computers anthropomorphically, as computational brains with psychological properties, while brain and cognitive sciences have ended up describing brains and minds computationally and informationally, as (...) biological computers. The crosswiring between the technical languages of these disciplines is not merely metaphorical but can lead to confusion, and damaging assumptions and consequences. The article ends on an optimistic note about the self-adjusting nature of technical meanings in language and the ability to leave misleading conceptual baggage behind when confronted with advancement in understanding and factual knowledge. (shrink)

We discuss here one of our projects, aimed at developing an automatic facial expression interpreter, mainly in terms of signaled emotions. We present some of the relevant findings on facial expressions from cognitive science and psychology that can be understood by and be useful to researchers in Human-Computer Interaction and Artificial Intelligence. We then give an overview of HCI applications involving automated facial expression recognition, we survey some of the latest progresses in this area reached by various approaches in (...) computer vision, and we describe the design of our facial expression recognizer. We also give some background knowledge about our motivation for understanding facial expressions and we propose an architecture for a multimodal intelligent interface capable of recognizing and adapting to computer users' affective states. Finally, we discuss current interdisciplinary issues and research questions which will need to be addressed for further progress to be made in the promising area of computational facial expression recognition. (shrink)

Recent progress in artificial intelligence (AI) has drawn attention to the technology’s transformative potential, including what some see as its prospects for causing large-scale harm. We review two influential arguments purporting to show how AI could pose catastrophic risks. The first argument — the Problem of Power-Seeking — claims that, under certain assumptions, advanced AI systems are likely to engage in dangerous power-seeking behavior in pursuit of their goals. We review reasons for thinking that AI systems might seek power, (...) that they might obtain it, that this could lead to catastrophe, and that we might build and deploy such systems anyway. The second argument claims that the development of human-level AI will unlock rapid further progress, culminating in AI systems far more capable than any human — this is the Singularity Hypothesis. Power-seeking behavior on the part of such systems might be particularly dangerous. We discuss a variety of objections to both arguments and conclude by assessing the state of the debate. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. (...) John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. John (...) Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

Conversational artificial intelligence (CAI) presents many opportunities in the psychotherapeutic landscape—such as therapeutic support for people with mental health problems and without access to care. The adoption of CAI poses many risks that need in-depth ethical scrutiny. The objective of this paper is to complement current research on the ethics of AI for mental health by proposing a holistic, ethical, and epistemic analysis of CAI adoption. First, we focus on the question of whether CAI is rather a tool or (...) an agent. This question serves as a framework for the subsequent ethical analysis of CAI focusing on topics of (self-) knowledge, (self-)understanding, and relationships. Second, we propose further conceptual and ethical analysis regarding human-AI interaction and argue that CAI cannot be considered as an equal partner in a conversation as is the case with a human therapist. Instead, CAI’s role in a conversation should be restricted to specific functions. (shrink)

Applications of artificial intelligence (AI) for cybersecurity tasks are attracting greater attention from the private and the public sectors. Estimates indicate that the market for AI in cybersecurity will grow from US$1 billion in 2016 to a US$34.8 billion net worth by 2025. The latest national cybersecurity and defence strategies of several governments explicitly mention AI capabilities. At the same time, initiatives to define new standards and certification procedures to elicit users’ trust in AI are emerging on a global (...) scale. However, trust in AI (both machine learning and neural networks) to deliver cybersecurity tasks is a double edged sword: it can improve substantially cybersecurity practices, but can also facilitate new forms of attacks to the AI applications themselves, which may pose severe security threats. We argue that trust in AI for cybersecurity is unwarranted and that, to reduce security risks, some form of control to ensure the deployment of ‘reliable AI’ for cybersecurity is necessary. To this end, we offer three recommendations focusing on the design, development and deployment of AI for cybersecurity. (shrink)

It has become a truism that the ethics of artificial intelligence (AI) is necessary and must help guide technological developments. Numerous ethical guidelines have emerged from academia, industry, government and civil society in recent years. While they provide a basis for discussion on appropriate regulation of AI, it is not always clear how these ethical guidelines were developed, and by whom. Using content analysis, we surveyed a sample of the major documents (_n_ = 47) and analyzed the accessible information (...) regarding their methodology and stakeholder engagement. Surprisingly, only 38% report some form of stakeholder engagement (with 9% involving citizens) and most do not report their methodology for developing normative insights (15%). Our results show that documents with stakeholder engagement develop more comprehensive ethical guidance with greater applicability, and that the private sector is least likely to engage stakeholders. We argue that the current trend for enunciating AI ethical guidance not only poses widely discussed challenges of applicability in practice, but also of transparent development (as it rather behaves as a black box) and of active engagement of diversified, independent and trustworthy stakeholders. While most of these documents consider people and the common good as central to their telos, engagement with the general public is significantly lacking. As AI ethics moves from the initial race for enunciating general principles to more sustainable, inclusive and practical guidance, stakeholder engagement and citizen involvement will need to be embedded into the framing of ethical and societal expectations towards this technology. (shrink)

In this chapter, I draw on my previous work on trust and cybersecurity to offer a definition of trust and trustworthiness to understand to what extent trusting AI for cybersecurity tasks is justified and what measures can be put in place to rely on AI in cases where trust is not justified, but the use of AI is still beneficial.

1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..

Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims to: (...) (i) defend the need for a novel conceptual model for understanding the systemic legal disruption caused by new technologies such as AI; (ii) to situate this model in relation to preceding debates about the interaction of regulation with new technologies (particularly the ‘cyberlaw’ and ‘robolaw’ debates); and (iii) to set out a detailed model for understanding the legal disruption precipitated by AI, examining both pathways stemming from new affordances that can give rise to a regulatory ‘disruptive moment’, as well as the Legal Development, Displacement or Destruction that can ensue. The article proposes that this model of legal disruption can be broadly generalisable to understanding the legal effects and challenges of other emerging technologies. (shrink)

The enduring innovations in artificial intelligence and robotics offer the promised capacity of computer consciousness, sentience and rationality. The development of these advanced technologies have been considered to merit rights, however these can only be ascribed in the context of commensurate responsibilities and duties. This represents the discernable next-step for evolution in this field. Addressing these needs requires attention to the philosophical perspectives of moral responsibility for artificial intelligence and robotics. A contrast to the moral status of animals (...) may be considered. At a practical level, the attainment of responsibilities by artificial intelligence and robots can benefit from the established responsibilities and duties of human society, as their subsistence exists within this domain. These responsibilities can be further interpreted and crystalized through legal principles, many of which have been conserved from ancient Roman law. The ultimate and unified goal of stipulating these responsibilities resides through the advancement of mankind and the enduring preservation of the core tenets of humanity. (shrink)

Since around 2012, there has been a ‘deep learning revolution’ in artificial intelligence (AI) that has brought AI to the forefront of many sectors of human activity. As new AI technology has sprea...

The increasing use of Artificial Intelligence for making decisions in public affairs has sparked a lively debate on the benefits and potential harms of self-learning technologies, ranging from the hopes of fully informed and objectively taken decisions to fear for the destruction of mankind. To prevent the negative outcomes and to achieve accountable systems, many have argued that we need to open up the “black box” of AI decision-making and make it more transparent. Whereas this debate has primarily focused (...) on how transparency can secure high-quality, fair, and reliable decisions, far less attention has been devoted to the role of transparency when it comes to how the general public come to perceive AI decision-making as legitimate and worthy of acceptance. Since relying on coercion is not only normatively problematic but also costly and highly inefficient, perceived legitimacy is fundamental to the democratic system. This paper discusses how transparency in and about AI decision-making can affect the public’s perception of the legitimacy of decisions and decision-makers and produce a framework for analyzing these questions. We argue that a limited form of transparency that focuses on providing justifications for decisions has the potential to provide sufficient ground for perceived legitimacy without producing the harms full transparency would bring. (shrink)

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which (...) draws attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency. (shrink)

Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...) and procedures cannot be meaningfully understood by human practitioners. When AI systems reach this level of complexity, we can also speak of black-box medicine. In this paper, we want to argue that black-box medicine conflicts with core ideals of patient-centered medicine. In particular, we claim, black-box medicine is not conducive for supporting informed decision-making based on shared information, shared deliberation, and shared mind between practitioner and patient. (shrink)

The idea that human thinking and machine computing are "radically the same" provides the central theme for this marvelously lucid and witty book on...

This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values in to AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used (...) to further strengthen design coordination efforts. VSD is shown to be both able to distill these common values as well as provide a framework for stakeholder coordination. (shrink)

In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal (...) values. Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens. (shrink)

In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of a (...) ‘good AI society’; the role and responsibility of the government, the private sector, and the research community in pursuing such a development; and where the recommendations to support such a development may be in need of improvement. Our analysis concludes that the reports address adequately various ethical, social, and economic topics, but come short of providing an overarching political vision and long-term strategy for the development of a ‘good AI society’. In order to contribute to fill this gap, in the conclusion we suggest a two-pronged approach. (shrink)

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated (...) credit-scoring processes “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that connect (...) human brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

The world’s current model for economic development is unsustainable. It encourages high levels of resource extraction, consumption, and waste that undermine positive environmental outcomes. Transitioning to a circular economy (CE) model of development has been proposed as a sustainable alternative. Artificial intelligence (AI) is a crucial enabler for CE. It can aid in designing robust and sustainable products, facilitate new circular business models, and support the broader infrastructures needed to scale circularity. However, to date, considerations of the ethical implications (...) of using AI to achieve a transition to CE have been limited. This article addresses this gap. It outlines how AI is and can be used to transition towards CE, analyzes the ethical risks associated with using AI for this purpose, and supports some recommendations to policymakers and industry on how to minimise these risks. (shrink)

In March 2016, DeepMind's computer programme AlphaGo surprised the world by defeating the world‐champion Go player, Lee Sedol. AlphaGo exhibits a novel, surprising and valuable style of play and has been recognised as “creative” by the artificial intelligence (AI) and Go communities. This article examines whether AlphaGo engages in creative problem solving according to the standards of comparative psychology. I argue that AlphaGo displays one important aspect of creative problem solving (namely mental scenario building in the form of Monte (...) Carlo tree search), while lacking another (domain generality). This analysis has consequences for how we think about creativity in humans and AI. (shrink)

Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies shows how current views on scientific method are challenged by this recent research, and suggests a new framework for the study of logic. Finally, he draws on work by such seminal thinkers as Bacon, Gdel, Popper, Penrose, and Lucas, to address the hotly-contested question of whether computers (...) might become intellectually superior to human beings. (shrink)

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in healthcare as a case study, we argue that AI bias is a form (...) of structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is about pursuing a more just social structure with the development and usage of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that connect (...) human brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

Artificial intelligence (AI) based clinical decision support systems (CDSS) are becoming ever more widespread in healthcare and could play an important role in diagnostic and treatment processes. For this reason, AI‐based CDSS has an impact on the doctor–patient relationship, shaping their decisions with its suggestions. We may be on the verge of a paradigm shift, where the doctor–patient relationship is no longer a dual relationship, but a triad. This paper analyses the role of AI‐based CDSS for shared decision‐making to (...) better comprehend its promises and associated ethical issues. Moreover, it investigates how certain AI implementations may instead foster the inappropriate paradigm of paternalism. Understanding how AI relates to doctors and influences doctor–patient communication is essential to promote more ethical medical practice. Both doctors' and patients' autonomy need to be considered in the light of AI. (shrink)

This paper looks at philosophical questions that arise in the context of AI alignment. It defends three propositions. First, normative and technical aspects of the AI alignment problem are interrelated, creating space for productive engagement between people working in both domains. Second, it is important to be clear about the goal of alignment. There are significant differences between AI that aligns with instructions, intentions, revealed preferences, ideal preferences, interests and values. A principle-based approach to AI alignment, which combines these elements (...) in a systematic way, has considerable advantages in this context. Third, the central challenge for theorists is not to identify ‘true’ moral principles for AI; rather, it is to identify fair principles for alignment that receive reflective endorsement despite widespread variation in people’s moral beliefs. The final part of the paper explores three ways in which fair principles for AI alignment could potentially be identified. (shrink)