This paper undertakes a comparative legal study to analyze the challenges of privacy and personal data protection posed by Artificial Intelligence embedded in Robots, and to offer policy suggestions. After identifying the benefits from various AI usages and the risks posed by AI-related technologies, I then analyze legal frameworks and relevant discussions in the EU, USA, Canada, and Japan, and further consider the efforts of Privacy by Design originating in Ontario, Canada. While various AI usages provide great convenience, (...) many issues, including profiling, discriminatory decisions, lack of transparency, and impeding consent, have emerged. The unpredictability arising from the AI machine learning function poses further difficulties, which have only been partially addressed by legal frameworks in the aforementioned jurisdictions. However, analyzing the relevant discussions yielded several suggestions. The first priority is adopting PbD as the most flexible, soft-legal, and preferable approach toward AI-oriented issues. Implementing PbD will protect individual privacy and personal data without specific efforts, and achieve both the development of AI and the advancement of privacy and personal data protection. Technical measures that can adapt to an individual’s dynamic choices according to the “context” should be further developed. Furthermore, alternative technical measures, including those to solve the “algorithmic black box” or achieve differential privacy, warrant thorough examination. If AI surpasses human intelligence, a terminating function, such as a “kill switch” will be the last resort to preserve individual choice. Despite numerous difficulties, we must prepare for the coming AI-prevalent society by taking a flexible approach. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the (...) subjective nature of ethical decision-making. The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the (...) subjective nature of ethical decision-making. The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

This article reviews the reasons scholars hold that driverless cars and many other AI equipped machines must be able to make ethical decisions, and the difficulties this approach faces. It then shows that cars have no moral agency, and that the term ‘autonomous’, commonly applied to these machines, is misleading, and leads to invalid conclusions about the ways these machines can be kept ethical. The article’s most important claim is that a significant part of the challenge posed by AI-equipped machines (...) can be addressed by the kind of ethical choices made by human beings for millennia. Ergo, there is little need to teach machines ethics even if this could be done in the first place. Finally, the article points out that it is a grievous error to draw on extreme outlier scenarios—such as the Trolley narratives—as a basis for conceptualizing the ethical issues at hand. (shrink)

In this paper, I examine whether the use of artificial intelligence (AI) and automated decision-making (ADM) aggravates issues of discrimination as has been argued by several authors. For this purpose, I first take up the lively philosophical debate on discrimination and present my own definition of the concept. Equipped with this account, I subsequently review some of the recent literature on the use AI/ADM and discrimination. I explain how my account of discrimination helps to understand that the general (...) claim in view of the aggravation of discrimination is unwarranted. Finally, I argue that the use of AI/ADM can, in fact, increase issues of discrimination, but in a different way than most critics assume: it is due to its epistemic opacity that AI/ADM threatens to undermine our moral deliberation which is essential for reaching a common understanding of what should count as discrimination. As a consequence, it turns out that algorithms may actually help to detect hidden forms of discrimination. (shrink)

New types of artificial intelligence products are gradually transferring to voice interaction modes with the demand for intelligent products expanding from communication to recognizing users' emotions and instantaneous feedback. At present, affective acoustic models are constructed through deep learning and abstracted into a mathematical model, making computers learn from data and equipping them with prediction abilities. Although this method can result in accurate predictions, it has a limitation in that it lacks explanatory capability; there is an urgent need (...) for an empirical study of the connection between acoustic features and psychology as the theoretical basis for the adjustment of model parameters. Accordingly, this study focuses on exploring the differences between seven major “acoustic features” and their physical characteristics during voice interaction with the recognition and expression of “gender” and “emotional states of the pleasure-arousal-dominance (PAD) model.” In this study, 31 females and 31 males aged between 21 and 60 were invited using the stratified random sampling method for the audio recording of different emotions. Subsequently, parameter values of acoustic features were extracted using Praat voice software. Finally, parameter values were analyzed using a Two-way ANOVA, mixed-design analysis in SPSS software. Results show that gender and emotional states of the PAD model vary among seven major acoustic features. Moreover, their difference values and rankings also vary. The research conclusions lay a theoretical foundation for AI emotional voice interaction and solve deep learning's current dilemma in emotional recognition and parameter optimization of the emotional synthesis model due to the lack of explanatory power. (shrink)

Much scholarly attention has recently been devoted to ways in which artificial intelligence (AI) might weaken formal political democracy, but little attention has been devoted to the effect of AI on “cultural democracy”—that is, democratic control over the forms of life, aesthetic values, and conceptions of the good that circulate in a society. This work is the first to consider in detail the dangers that AI-driven cultural recommendations pose to cultural democracy. This Article argues that AI threatens to (...) weaken cultural democracy by undermining individuals’ direct and spontaneous engagement with a diverse range of cultural materials. It further contends that United States law, in its present form, is ill equipped to address these challenges, and suggests several strategies for better regulating culture-mediating AI. Finally, it argues that while such regulations might run afoul of contemporary First Amendment doctrine, the most normatively attractive interpretation of the First Amendment not only allows but encourages such interventions. (shrink)

Policymakers need to start considering the impact smart connected toys (SCTs) have on children. Equipped with sensors, data processing capacities, and connectivity, SCTs targeting children increasingly penetrate pervasively personal environments. The network of SCTs forms the Internet of Toys (IoToys) and often increases children's engagement and playtime experience. Unfortunately, this young part of the population and, most of the time, their parents are often unaware of SCTs’ far-reaching capacities and limitations. The capabilities and constraints of SCTs create severe side effects (...) at the technical, individual, and societal level. These side effects are often unforeseeable and unexpected. They arise from the technology's use and the interconnected nature of the IoToys, without necessarily involving malevolence from their creators. Although existing regulations and new ethical guidelines for artificial intelligence provide remedies to address some of the side effects, policymakers did not develop these redress mechanisms having children and SCTs in mind. This article provides an analysis of the arising side effects of SCTs and contrasts them with current regulatory redress mechanisms. We thereby highlight misfits and needs for further policymaking efforts. (shrink)

Policymakers need to start considering the impact smart connected toys (SCTs) have on children. Equipped with sensors, data processing capacities, and connectivity, SCTs targeting children increasingly penetrate pervasively personal environments. The network of SCTs forms the Internet of Toys (IoToys) and often increases children's engagement and playtime experience. Unfortunately, this young part of the population and, most of the time, their parents are often unaware of SCTs’ far-reaching capacities and limitations. The capabilities and constraints of SCTs create severe side effects (...) at the technical, individual, and societal level. These side effects are often unforeseeable and unexpected. They arise from the technology's use and the interconnected nature of the IoToys, without necessarily involving malevolence from their creators. Although existing regulations and new ethical guidelines for artificial intelligence provide remedies to address some of the side effects, policymakers did not develop these redress mechanisms having children and SCTs in mind. This article provides an analysis of the arising side effects of SCTs and contrasts them with current regulatory redress mechanisms. We thereby highlight misfits and needs for further policymaking efforts. (shrink)

The aim of this article is to articulate and critically discuss different answers to the following question: How should decision-makers deal with conflicts that arise when the values usually entailed in ethical guidelines – such as accuracy, privacy, non-discrimination and transparency – for the use of Artificial Intelligence clash with one another? To begin with, I focus on clarifying some of the general advantages of using such guidelines in an ethical analysis of the use of AI. Some disadvantages (...) will also be presented and critically discussed. Second, I will show that we need to distinguish between three kinds of conflict that can exist for ethical guidelines used in the moral assessment of AI. This section will be followed by a critical discussion of different answers to the question of how to handle what we shall call internal and external values conflicts. Finally, I will wrap up with a critical discussion of three different strategies to resolve what is called a ‘genuine value conflict’. These strategies are: the ‘accepting the existence of irresolvable conflict’ view, the ranking view, and value monism. This article defends the ‘accepting the existence of irresolvable conflict’ view. It also argues that even though the ranking view and value monism, from a merely theoretical point of view, are better equipped to solve genuine value conflicts among values in ethical guidelines for artificial intelligence, this is not the case in real-life decision-making. Keywords: AI; ethical guidelines; algorithm-based sentencing; value conflicts. (shrink)

Comprehensively improving the level of vocational education and teaching quality has become an important initiative to meet the new round of technological revolution and industrial change. The traditional teaching mode can no longer meet the needs of industries and enterprises for job competences, and all higher education institutions are actively thinking about how to carry out teaching reform. Virtual reality can effectively solve the above-mentioned drawbacks, but the hardware facilities of the existing VR systems are extremely expensive, making it impossible (...) to popularize it in teaching. In this context, this paper is based on VR for the design of an interactive teaching platform. This paper discusses the key technologies of virtual reality, introduces the relevant theoretical foundation, and describes the current virtual reality devices with high popularity and their advantages and disadvantages for systematic analysis. From the perspective of teaching design, the design of human-computer interaction teaching process using VR technology to develop learning scenes is studied, introducing 3D modeling techniques, model construction, the production of picture and text panels, the production of video materials, and the establishment of virtual tour guide 3D scenes. The principle of this paper is to build a 3D vision for building 3D views of educational needs to design high quality images and video animation using 3DMAX and unity 3D engines. Then, we introduce the use of VR glasses, smart phones, and Bluetooth wireless handle to build a simple interactive teaching platform; the platform is of low cost of construction and student coverage, to solve the teaching resources shortage, equipment conditions lagging behind, the curriculum content aging, teachers and students who cannot interact in real time, and a series of problems. (shrink)

To assist in the evaluation process when determining architectures for new robots and intelligent systems equipped with artificial emotions, it is beneficial to understand the systems that have been built previously. Other surveys have classified these systems on the basis of their technological features. In this survey paper, we present a classification system based on a model similar to that used in psychology and philosophy for theories of emotion. This makes possible a connection to thousands of years of discourse (...) on the topic of emotion. Five theories of emotion are described based on an emotion theory model proposed by Power and Dalgleish. The paper provides classifications using a model of 10 new questions, for 14 major research projects that describe implementations or designs for systems that use artificial emotions for either robotics or general artificial intelligence. We also analyze the trends in the usage of various theories and complexity changes over time. (shrink)

In order to improve early childhood physical education, in this study, the talent cultivation mechanism for undergraduates was explored under the “full-practice” concept, oriented by preschooler mental health. First, from the perspective of preschooler psychology, the mechanisms of ability training and talent cultivation for undergraduates majoring in early childhood education were explored under the “full-practice” concept. Considering that the physical, psychological, and intellectual development of preschoolers shall follow the rules of physical education, and current early childhood education mainly focuses on (...) intelligence education in China, early childhood physical education was analyzed further in this study. By investigating the undergraduate majors of early childhood education in Henan University, this study first summarized the current problems in early childhood education systems in universities. Secondly, combined with the form of physical education in kindergartens, strategies for talent cultivation and curriculum setting of early childhood physical education majors in colleges and universities were proposed. Finally, from the perspective of innovation and diversification of training forms, the cultivation of early childhood educators’ physical education ability was analyzed at multiple levels and multiple objectives, and the integrated training system of early childhood education talents was constructed. The results show that, among all the courses for early childhood education major, compulsory courses account for 81.2% and optional courses account for 18.8%. In addition, a survey on undergraduates’ attitudes toward the curriculum of their major demonstrates that 81.2% of the undergraduates thought that the range and content of practical courses should be increased, indicating that undergraduates majoring in early childhood education are dissatisfied with the current curriculum system, and they have an increased demand for practical courses. Correspondingly, it is vital to build and improve on the early childhood physical education. In terms of its talent cultivation, the “full-practice” concept helps combine theory with practice to improve the effectiveness of education and teaching, pushing forward the reform of the education system. Meanwhile, data- and intelligence-oriented teaching will become the new direction of modern sports development, as well as an important link for tracking and monitoring children’s sports teaching in China. Through the continuous introduction of wearable artificial intelligence products, real-time monitoring of children’s physical conditions can be realized, which helps improve the effectiveness of early childhood physical education. (shrink)

As robots and Artificial Intelligences become more enmeshed in rich social contexts, it seems inevitable that we will have to make them into moral machines equipped with moral skills. Apart from the technical difficulties of how we could achieve this goal, we can also ask the ethical question of whether we should seek to create such Artificial Moral Agents (AMAs). Recently, several papers have argued that we have strong reasons not to develop AMAs. In response, we develop a (...) comprehensive analysis of the relevant arguments for and against creating AMAs, and we argue that all things considered we have strong reasons to continue to responsibly develop AMAs. The key contributions of this paper are threefold. First, to provide the first comprehensive response to the important arguments made against AMAs by Wynsberghe and Robbins (in “Critiquing the Reasons for Making Artificial Moral Agents”, Science and Engineering Ethics 25, 2019) and to introduce several novel lines of argument in the process. Second, to collate and thematise for the first time the key arguments for and against AMAs in a single paper. Third, to recast the debate away from blanket arguments for or against AMAs in general, to a more nuanced discussion about the use of what sort of AMAs, in what sort of contexts, and for what sort of purposes is morally appropriate. (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)

본 논문의 목적은 현재의 인공지능(artificial intelligence, 이하 AI) 윤리 논의에서 결여되어 있는 AI 개발자 윤리의 중요성과 필요성을 부각하고, AI 개발자 윤리로 덕성(virtue)기반의 모델이 적합함을 보여 이를 제안하는 것이다. AI는 고정된 과학기술이 아니라 발전노정에 있는 동적이라는 특성과 인간과의 상호작용에서 AI를 만든 인간의 삶에 지대한 영향을 끼치는 재귀적 특성이 있는 과학기술이다. 이러한 AI의 특성으로 인해, AI가 초래할 리스크에 대응하려는 AI 윤리는 발전 과정에 따라 제시되어야 한다는 점에서 체계적이고, 실천을 도모하는 동기부여의 구조를 가져야 한다는 점에서 기존 도덕의 형식적 특징인 1인칭, 2인칭, (...) 3인칭 관점이 통합될 필요가 있다. 그런데 최근에 활발하게 제시된 많은 AI윤리 지침에 대한 메타 분석에 따르면, 현재의 지침으로서의 AI 윤리 논의들은 1인칭 관점의 동기부여가 없는 3인칭 관점에만 한정되어 실질적인 ‘윤리 실현’(ethics realizing)을 도모하기에 취약하다. AI 윤리 지침 자체가 추상적이고 모호할 뿐만 아니라, AI를 개발하는 사람들에게 아무런 영향력을 행사하지 못하는 ‘윤리 세탁’(ethics washing)에 불과하기 때문이다. 이러한 한계를 보완하기 위해서는 AI 개발자들이 AI 윤리를 내면화하여 규범적으로 실천하게 할 수 있게 하는 AI 개발자 윤리가 필요한데, 본 논문은 덕성 기반의 AI 개발자 윤리가 이러한 목적을 위해 효과적이라고 주장한다. 덕성 기반의 AI 개발자 모델은, 과학적 덕성 모델(scientific virtue model)이 그랬던 것처럼 AI 개발의 목적이 무엇인지, AI개발자들의 전문직 덕성(vocational virtue)이 무엇인지, 그리고 이러한 덕성을 함양하기 위한 효과적인 교육 방법은 무엇인지 등의 이론 연구와 AI 개발자들과의 소통을 토대로 하는 경험 연구의 결합을 통해 실천적 차원의 모델로 구체화될 수 있다. 본 논문에서 이론적으로 제안하는 덕성 기반의 AI 개발자 윤리가 추후 한국 사회의 경험 연구를 통해 실천적 차원의 모델로 구체화되어 AI가 초래하는 윤리적 문제들에 효과적으로 대응하면서 AI의 혜택이 풍성히 향유되는데 기여하기를 기대한다. (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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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

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)

인공지능에 인격의 지위를 부여할 수 있는지에 대한 찬반 논쟁에서, 인격에 대한 칸트의 관점이 종종 인용된다. 인공지능에 인격을 부여할 수 있는지에 대한 찬성 입장과 반대 입장 사이에 여러 상이한 주장들이 발견되는 한편, 양측은 종종 인격에 대한 칸트의 관점에 따르면 인공지능에는 인격이 부여될 수 없다는 해석을 공통적으로 받아들인다. 인공지능에 인격의 지위를 부여하는 것에 반대하는 입장에서는 칸트 철학에 대한 그러한 해석을 인공지능 일반에 인격의 지위를 부여해서는 안 된다는 주장을 뒷받침하는 철학적 논거 중 하나로 활용하는 반면, 인공지능에 인격의 지위를 부여하는 것에 찬성하는 입장에서는 칸트 (...) 철학에 대한 그러한 해석을 극복해야 할 인간 중심주의적 편견을 보여주는 한 사례로 활용한다. 본 논문은 찬반 양측이 종종 공유하는 해당 해석의 한계를 지적하고, 인격에 대한 칸트의 관점이 인공지능에 인격을 부여할 수 있는지의 문제에 어떠한 시사점을 줄 수 있는지에 대해 논의한다. 해당 해석은 특정 분야의 문제 해결을 위해서만 활용될 수 있는 약 인공지능에 적용되는 경우 어느 정도 설득력이 있으나, 이성적 사유와 도덕적 행위를 수행할 수 있을 것으로 상상되는 강 인공지능까지 논의의 범위에 포함되는 경우 문제적이다. 해당 해석은 칸트가 인격을 이성적 사유와 도덕적 행위의 가능성을 뒷받침하는 표상으로 제시할 뿐, 인격의 표상이 적용될 수 있는 실제 존재자의 범위에 대한 객관적 지식을 주장하지 않는다는 점을 종종 간과한다. 칸트는 인격에 대한 객관적 지식이 성립될 수 없다고 주장하므로, 인격에 대한 칸트의 관점은 인격적 존재자를 인간종의 범위 안으로 국한시키는 인간 중심주의와는 거리가 멀며, 이성적 사유와 도덕적 행위를 수행할 수 있을 것으로 상상되는 강인공지능의 출현 가능성을 부정할 논거로 활용되기 어렵다. (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)

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...

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)

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)

Artificial intelligence (AI) national strategies provide countries with a framework for the development and implementation of AI technologies. Sixty countries worldwide published their AI national strategies. The majority of these countries with more than 70% are developed countries. The approach of AI national strategies differentiates between developed and developing countries in several aspects including scientific research, education, talent development, and ethics. This paper examined AI readiness assessment in a developing country (Palestine) to help develop and identify the main (...) pillars of the AI national strategy. AI readiness assessment was applied across education, entrepreneurship, government, and research and development sectors in Palestine (case of a developing country). In addition, it examined the legal framework and whether it is coping with trending technologies. The results revealed that Palestinians have low awareness of AI. Moreover, AI is barely used across several sectors and the legal framework is not coping with trending technologies. The results helped develop and identify the following five main pillars that Palestine’s AI national strategy should focus on: AI for Government, AI for Development, AI for Capacity Building in the private, public and technical and governmental sectors, AI and Legal Framework, and international Activities. (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)

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)

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)

인공 감정에 관한 철학적 탐구가 필요한 시점이다. 인간의 고유한 영역으로 간주되던 인지적 과제에서 기계의 추월을 염려하는 처지가 되자, 사람들은 이제 이성이 아니라 감정에서 인간의 고유성을 찾으려한다. 하지만 최근 인공지능 로봇에 감성을 불어넣는 작업이 새로운 화두로 떠오르고 있다. 이 글은 인공 감정의 실현가능성과 잠재적인 위험을 논의한다. 먼저, 감성 로봇의 개발 현황과 주요한 동기들을 개괄하고, 왜 로봇의 감정이 문제가 되는지 살펴본다. 진정한 감정-소유 로봇이 가능한지 검토하기 위해 감정을 선험적으로 정의하기보다는 감정이 수행하는 몇 가지 핵심 역할을 소개하고 이로부터 어떤 대상에 감정을 부여할 수 (...) 있는 기준들을 제안한다. 나는 이런 기준에 비추어 진정한 감정 로봇이 근미래에 실현될 가능성이 낮다고 주장한다. 그러나 감정-소유 로봇이 등장하기 이전이라도, 어느 정도의 자율성을 가진 로봇과 맺는 일방적 감정 소통은 잠재적으로 위험할 수 있으며, 이에 대비하는 것이 시급하다고 주장한다. (shrink)

The purpose of this book, originally published in 1987, was to contribute to the advance of artificial intelligence by clarifying and removing the major sources of philosophical confusion at the time which continued to preoccupy scientists and thereby impede research. Unlike the vast majority of philosophical critiques of AI, however, each of the authors in this volume has made a serious attempt to come to terms with the scientific theories that have been developed, rather than attacking superficial 'straw (...) men' which bear scant resemblance to the complex theories that have been developed. For each is convinced that the philosopher's responsibility is to contribute from his own special intellectual point of view to the progress of such an important field, rather than sitting in lofty judgement dismissing the efforts of their scientific peers. The aim of this book is thus to correct some of the common misunderstandings of its subject. The technical term Artificial Intelligence has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike. has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike. inform laymen and specialists alike. (shrink)

A major approach to the ethics of artificial intelligence is to use social choice, in which the AI is designed to act according to the aggregate views of society. This is found in the AI ethics of “coherent extrapolated volition” and “bottom–up ethics”. This paper shows that the normative basis of AI social choice ethics is weak due to the fact that there is no one single aggregate ethical view of society. Instead, the design of social choice AI (...) faces three sets of decisions: standing, concerning whose ethics views are included; measurement, concerning how their views are identified; and aggregation, concerning how individual views are combined to a single view that will guide AI behavior. These decisions must be made up front in the initial AI design—designers cannot “let the AI figure it out”. Each set of decisions poses difficult ethical dilemmas with major consequences for AI behavior, with some decision options yielding pathological or even catastrophic results. Furthermore, non-social choice ethics face similar issues, such as whether to count future generations or the AI itself. These issues can be more important than the question of whether or not to use social choice ethics. Attention should focus on these issues, not on social choice. (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)