Dans cet article, nous interrogeons la désuétude du projet initial de l’intelligence artificielle, fondé conjointement avec la science cognitive, et partageant avec elle ce qu’on appela plus tard l’hypothèse computationnelle de l’esprit, c’est-à-dire l’idée que la pensée intelligente peut être décrite sous la forme de programmes informatiques. Si cette désuétude reflète en partie notre éloignement d’une période très particulière du xxe siècle, celle des années 1950 marquées par les angoisses de la guerre froide, nous souhaitons montrer qu’elle est sous-tendue (...) par une désuétude plus profonde encore, celle d’une conception hétérodoxe, voire « alchimique », de la science. Malgré son abandon au cours des années 1980, nous explorons l’idée que cette hypothèse reste tout de même vivace comme tentation congénitale au projet même de la science cognitive, en examinant les thèses récentes de Gualtiero Piccinini qui considère les mécanismes neuronaux comme une forme élargie de calcul. (shrink)

Book review of: Vincent C. Müller, _Fundamental issues of artificial intelligence_, Springer International Publishing AG, 2016, pp. 570.

The article describes the author's approach to the construction of general-level artificial cognitive agents based on the so-called "semantic supervised learning", within which, in accordance with the hybrid paradigm of artificial intelligence, both machine learning methods and methods of the symbolic ap­ proach and knowledge-based systems are used ("good old-fashioned artificial intelligence"). А descrip­ tion of current proЬlems with understanding of the general meaning and context of situations in which narrow AI agents are (...) found is presented. The definition of semantic supervised learning is given and its relationship with other machine learning methods is described. In addition, а thought experiment is presented, which shows the essence and meaning of supervised semantic learning. (shrink)

Artificial intelligence is increasingly permeating many types of high-stake societal decision-making such as the work at the criminal courts. Various types of algorithmic tools have already been introduced into sentencing. This article concerns the use of algorithms designed to deliver sentence recommendations. More precisely, it is considered how one should determine whether one type of sentencing algorithm (e.g., a model based on machine learning) would be ethically preferable to another type of sentencing algorithm (e.g., a model based on (...) old-fashioned programming). Whether the implementation of sentencing algorithms is ethically desirable obviously depends upon various questions. For instance, some of the traditional issues that have received considerable attention are algorithmic biases and lack of transparency. However, the purpose of this article is to direct attention to a further challenge that has not yet been considered in the discussion of sentencing algorithms. That is, even if is assumed that the traditional challenges concerning biases, transparency, and cost-efficiency have all been solved or proven insubstantial, there will be a further serious challenge associated with the comparison of sentencing algorithms; namely, that we do not yet possess an ethically plausible and applicable criterion for assessing how well sentencing algorithms are performing. (shrink)

Solipsism, Individualism and Cognitive Science [1] "Artificial Intelligence cannot ignore philosophy" - John McCarthy I shall challenge the claim that Good Old-Fashioned Artificial Intelligence, or GOFAI is solipsistic while more recent neural or "brain-style" approaches to AI are not. After distinguishing GOFAI from connectionism, I will first show that GOFAI is not committed to solipsism but rather to what is more properly called individualism.

Starting in the 1950s, computer programs for simulating cognitive processes and intelligent behaviour were the hallmark of Good Old-Fashioned Artificial Intelligence and ‘cognitivist’ cognitive science. This article examines a somewhat neglected case of simulation pursued by one of the founding fathers of simulation methodology, Herbert A. Simon. In the 1970s and 1980s, Simon had repeated contacts with Marxist countries and scientists, in the context of which he advanced the idea that cognitivism could be used as a (...) framework for simulating dialectical materialism. Simon's idea was, in particular, to represent dialectical processes through a ‘symbolic’ version of dialectical logic. This article explores the context of Simon's interaction with Marxist countries—China and the USSR—and also assesses the outcome of the simulation. The difficulty with simulating distinctive features of dialectical materialism is read in light of the underlying assumptions of cognitivism and, ultimately, in light of the attempt to tame a rival world view. (shrink)

We are obsessed with self-improvement; it’s a billion-dollar industry. But apps, workshops, speakers, retreats, and life hacks have not made us happier. Obsessed with the endless task of perfecting ourselves, we have become restless, anxious, and desperate. We are improving ourselves to death. The culture of self-improvement stems from philosophical classics, perfectionist religions, and a ruthless strain of capitalism—but today, new technologies shape what it means to improve the self. The old humanist culture has given way to artificial (...) class='Hi'>intelligence, social media, and big data: powerful tools that do not only inform us but also measure, compare, and perhaps change us forever. This book shows how self-improvement culture became so toxic—and why we need both a new concept of the self and a mission of social change in order to escape it. Mark Coeckelbergh delves into the history of the ideas that shaped this culture, critically analyzes the role of technology, and explores surprising paths out of the self-improvement trap. Digital detox is no longer a viable option and advice based on ancient wisdom sounds like yet more self-help memes: The only way out is to transform our social and technological environment. Coeckelbergh advocates new “narrative technologies” that help us tell different and better stories about ourselves. However, he cautions, there is no shortcut that avoids the ancient philosophical quest to know yourself, or the obligation to cultivate the good life and the good society. (shrink)

Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI (...) is closely related to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

Handling Digital Brains proves that ethnography of the laboratory is still capable of making a significant contribution in the field of social studies on science and technology. The reviewed work presents details of interactions between researchers, as well as between researchers and their material equipment, which are key to explaining the methods of solving research problems when analyzing brain scans generated during fMRI experiments. Significantly, the reconstructed multimodal embodied practices shed light not only on the process of scientific cognition, but (...) also on a broader spectrum of human cognitive activities. The book constitutes a challenge of a kind to neurocognitive sciences. As the author shows, cognitive neuroscientists utilizing fMRI declare that they study the embodied mind; yet, in practice, they reduce the body to the brain, and cognition – to purely internal processes. Such a model of cognition, (tacitly) assumed by experimental neurocognitive scientists, turns out to be insufficient when used reflexively in order to explain the way neuroscientists themselves solve problems. (shrink)

Despite the modern trappings the values of the Sons of SAMCRO and their old ladies are even more traditional than those of mainstream society. The parallels between the culture depicted in Sons of Anarchy and the one depicted by Homer's epics make the show philosophically interesting, because moral philosophy in Greece began as a reaction against Homeric values. Just as the Sons bear the Reaper on their cuts, Homeric warriors often decorated their armor with violent images to make clear their (...) status as killers. Those who have drawn blood for the club wear the “Men of Mayhem” patch. There are many ways in which the Homeric heroes outstrip the brutality of SAMCRO. The parallels between SAMCRO's and Homer's conceptions of a good life show us both what is commendable and condemnable about life in the MC. (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)

The paper adopts an inter-theoretical socio-cultural and -material perspective on the relationship between human + machine learning to propose a new way to investigate the human + machine assistive assemblages emerging in professional work (e.g. medicine, architecture, design and engineering). Its starting point is Hutchins’s (1995a) concept of ‘distributed cognition’ and his argument that his concept of ‘cultural ecosystems’ constitutes a unit of analysis to investigate collective human + machine working and learning (Hutchins, Philos Psychol 27:39–49, 2013). It argues that: (...) (i) the former offers a way to reveal the cultural constitution of and enactment of human + machine cognition and, in the process, the limitations of the computational and connectionist assumptions about learning that underpin, respectively, good old-fashioned AI and deep learning; and (2) the latter offers a way to identify, when amplified with insights from Socio-Materialism and Cultural-Historical Activity Theory, how ML is further rearranging and reorganising the distributed basis of cognition in assistive assemblages. The paper concludes by outlining a set of conjectures researchers that could use to guide their investigations into the ongoing design and deployment of HL + ML assemblages and challenges associated with the interaction between HL + ML. (shrink)

To become more broadly applicable, positions on AI ethics require perspectives from non-Western regions and cultures such as China and Japan. In this paper, we propose that the addition of the concept of harmony to the discussion on ethical AI would be highly beneficial due to its centrality in East Asian cultures and its applicability to the challenge of designing AI for social good. We first present a synopsis of different definitions of harmony in multiple contexts, such as music (...) and society, which reveals that the concept is, at its core, about well-balanced relationships and appropriate actions which give rise to order, balance, and aesthetically pleasing phenomena. The mediator for these well-balanced relationships isTaktwhich is an ability to act thoughtfully and sensibly according to the specific situation and to put things into proportion and order. We propose that the central challenge of building harmonizing AI is to make intelligent systems tactful and also to design and use them tactfully. For an AI system to become tactful, it needs to be able to have an advanced sensitivity to the specific contexts which it is in and their social and ethical implications and have the capability of approximately inferring the emotional and cognitive states of people with whom it is interacting. (shrink)

Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in (...) marketing. To reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being. (shrink)

The age of artificial intelligence (AI) is upon us, and its effect upon society in the coming years will be noteworthy. Artificial intelligence is a field that encompasses such applications as robotics, expert systems, natural language understanding, speech recognition, and computer vision. The effect of these AI systems upon existing and future job occupations will be important. This paper takes a look at artificial intelligence in terms of the creation of new job categories. Also, (...) the introduction of AI into the organization to better familiarize the employees about AI will be discussed. (shrink)

The justification and rationality of this paper is to present some fundamental principles, theories, and concepts that we believe moulds the nucleus of a good artificial intelligence (AI) society. The morally accepted significance and utilitarian concerns that stems from the inception and realisation of an AI’s structural foundation are displayed in this study. This paper scrutinises the structural foundation, fundamentals, and cardinal righteous remonstrations, as well as the gaps in mechanisms towards novel prospects and perils in determining (...) resilient fundamentals, accountability, and AI’s convoluted and responsible implications. We outline a number of salient and practical benefits, in which to place moral norms within the mise en scène of AI, to delineate the rudimentary ethical dilemmas and decorous directions within the realms of AI. (shrink)

This article explores whether artificial intelligence (AI) can engage in the practice of law as an art of good and justice. It examines the historical and philosophical foundations of law as the art of promoting societal harmony and resolving moral dilemmas. The research employs critical and philosophical analysis methods integrating insights from legal scholars, ethicists, technologists, and policymakers. The study identifies AI’s potential to streamline legal processes, enhance access to justice, and reduce bias in decision-making. However, it (...) also highlights ethical challenges such as transparency, accountability, and the impact on the legal workforce. The article emphasises the importance of striking a balance between technological innovation and human values, advocating for proactive regulation and interdisciplinary cooperation to ensure the ethical development and implementation of AI in law. The results of the study highlight the transformative potential of AI in revolutionising legal practice, emphasising its capacity to streamline processes, improve access to justice, and mitigate bias. However, ethical considerations such as transparency, accountability, and the preservation of human judgment are crucial to ensuring that AI integration in law upholds fundamental principles of justice and fairness. (shrink)

BackgroundArtificial intelligence has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following (...) question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective?MethodsEight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed.ResultsUpon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries.ConclusionsThe ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere. (shrink)

In their historical overview of cognitive science, Bechtel, Abraham- son and Graham (1999) describe the field as expanding in focus be- ginning in the mid-1980s. The field had spent the previous 25 years on internalist, high-level GOFAI (“good old fashioned artificial intelli- gence” [Haugeland 1985]), and was finally moving “outwards into the environment and downards into the brain” (Bechtel et al, 1999, p.75). One important force behind the downward movement was Patricia Churchland’s Neurophilosophy (1986). This book began (...) a movement bearing its name, one that truly came of age in 1999 when Kath- leen Akins won a million-dollar fellowship to begin the McDonnell Project in Philosophy and the Neurosciences. The McDonnell Project put neurophilosophy at the forefront of philosophy of mind and cogni- tive science, yielding proliferating articles, conferences, special journal issues and books. In two major new books, neurophilosophers Patricia Churchland (2002) and John Bickle (2003) clearly feel this newfound prominence: Churchland mocks those who do not apply findings in neuroscience to philosophical problems as “no-brainers”; Bickle mocks anyone with traditional philosophical concerns, including “naturalistic philosophers of mind” and other neurophilosophers. (shrink)

AI/ML increasingly impacts the ability of humans to have a good life. Various sets of indicators exist to measure well-being/the ability to have a good life. Students play an important role in AI/ML discussions. The purpose of our study using an online survey was to learn about the perspectives of undergraduate STEM students on the impact of AI/ML on well-being/the ability to have a good life. Our study revealed that many of the abilities participants perceive to be (...) needed for having a good life were part of the well-being/ability to have a good life indicator lists we gave to participants. Participants perceived AI/ML to have and continue to have the most positive impact on the ability to have a good life for disabled people, elderly people, and individuals with a high income and the least positive impact for people of low income and countries from the global south. Regarding indicators of well-being and the ability to have a good life given to participants, we found a significant techno-positive sentiment. 30% of respondents selected the purely positive box for 28 of the indicators and none did so for the purely negative box. For 52 indicators, the purely negative was below 10% (not counting the 0%) and for 10 indicators, none selected purely negative. Our findings suggest that our questions might be valuable tools to develop an inventory of STEM and other students’ perspectives on the implications of AI/ML on the ability to have a good life. (shrink)

Insofar as artificial intelligence is to be used to guide automated systems in their interactions with humans, the dominant view is probably that it would be appropriate to programme them to maximize (expected) utility. According to utilitarianism, which is a characteristically western conception of moral reason, machines should be programmed to do whatever they could in a given circumstance to produce in the long run the highest net balance of what is good for human beings minus what (...) is bad for them. In this essay, I appeal to values that are characteristically African––but that will resonate with those from a variety of moral-philosophical traditions, particularly in the Global South––to cast doubt on a utilitarian approach. Drawing on norms salient in sub-Saharan ethics, I provide four reasons for thinking it would be immoral for automated systems governed by artificial intelligence to maximize utility. In catchphrases, I argue that utilitarianism cannot make adequate sense of the ways that human dignity, group rights, family first, and (surprisingly) self-sacrifice should determine the behaviour of smart machines. (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)

Artificial intelligence (AI) profoundly influences a number of societal structures today, including religious dynamics. Using Bernard Lonergan’s critical realism as a lens, this article investigates the intersections of AI and religious traditions in their shared pursuit of the common good. Beginning with Lonergan’s principle that humans construct their understanding through cognitive processes, we examine how AI-mediated realities align with or challenge traditional religious tenets. By delving into specific cases, we spotlight AI’s role in reshaping religious symbols, rituals, (...) and even creating novel spiritual meanings. Using Lonergan’s insights on the balance between subjectivity and objectivity, I analyze AI’s potential to both create new sacred spaces and challenge religious orthodoxy. The crux of the discussion centers on the negotiation between religious values and technological innovation, assessing how AI can bolster religious life while maintaining its core essence. Ultimately, this article underscores the importance of the common good in the age of AI-driven religious evolution. (shrink)

In response to Ferrario _et al_’s 1 work entitled ‘Ethics of the algorithmic prediction of goal of care preferences: from theory to practice’, we would like to point out an area of concern: the risk of artificial intelligence (AI) paternalism in their proposed framework. Accordingly, in this commentary, we underscore the importance of the implementation of safeguards for AI algorithms before they are deployed in clinical practice. The goal of documenting a living will and advanced directives is to (...) convey personal preferences regarding the acceptance of therapies, including life support, for future use in case of losing one’s capacity. This is standard practice in the care of incapacitated critically ill patients as it is considered to extend the individual’s autonomy. Notably, most of the documents that intensivists encounter in clinical practice are written in a generic fashion and lack context. This problem usually leads to the reliance on... (shrink)

This study challenges the conventional boundaries of philosophy by asserting that organizations can function as legitimate subjects within philosophical discourse. Western philosophy, epitomized by Descartes, has long assumed that individual human beings are the fundamental units of thought and moral agency. However, in a significant oversight, this belief overlooks the idea that organizations can think independently, leading to both virtuous and malevolent results. Epistemology lacks a clear prioritization of morally sound knowledge over potentially harmful knowledge. The advent of artificial (...) intelligence (AI) has also transformed the landscape of cognition from a primarily individual human endeavor into a joint undertaking where humans collaborate with AI entities in collective thinking and decision-making. Nevertheless, AI lacks the innate ability to discern between ethical and unethical actions. This symbiosis creates organizations as fundamental philosophical subjects, marking a significant shift in philosophical emphasis where the organization takes the center stage. Accordingly, within this emerging field, we advance four propositions as the core subjects of organization philosophy: organizations can think, organizations can create knowledge, AI can be part of an organization, and humans in the organization control moral discipline. These propositions guide the ethical coexistence of AI and human agents in organizations, emphasizing the importance of ethical deliberation in knowledge use. Moreover, the malicious use of profound knowledge hinders human progress, as illustrated in this study by an ethical examination of military institutions within organization philosophy. (shrink)

Although the concept of “AI governance” is frequently used in the debate, it is still rather undertheorized. Often it seems to refer to the mechanisms and structures needed to avoid “bad” outcomes and achieve “good” outcomes with regard to the ethical problems artificial intelligence is thought to actualize. In this article we argue that, although this outcome-focused view captures one important aspect of “good governance,” its emphasis on effects runs the risk of overlooking important procedural aspects (...) of good AI governance. One of the most important properties of good AI governance is political legitimacy. Starting out from the assumptions that AI governance should be seen as global in scope and that political legitimacy requires at least a democratic minimum, this article has a twofold aim: to develop a theoretical framework for theorizing the political legitimacy of global AI governance, and to demonstrate how it can be used as a compass for critially assessing the legitimacy of actual instances of global AI governance. Elaborating on a distinction between “governance by AI” and “governance of AI” in relation to different kinds of authority and different kinds of decision-making leads us to the conclusions that much of the existing global AI governance lacks important properties necessary for political legitimacy, and that political legitimacy would be negatively impacted if we handed over certain forms of decision-making to artificial intelligence systems. (shrink)

Artificial intelligence (AI) technologies are increasingly creeping into the work sphere, thereby gradually questioning and/or disturbing the long-established moral concepts and norms communities have been using to define what makes work good. Each community, and Muslims make no exception in this regard, has to revisit their moral world to provide well-thought frameworks that can engage with the challenging ethical questions raised by the new phenomenon of AI-mediated work. For a systematic analysis of the broad topic of AI-mediated (...) work ethics from an Islamic perspective, this article focuses on presenting an accessible overview of the “moral world” of work in the Islamic tradition. Three main components of this moral world were selected due to their relevance to the AI context, namely (1) Work is inherently good for humans, (2) Practising a religiously permitted profession and (c) Maintaining good relations with involved stakeholders. Each of these three components is addressed in a distinct section, followed by a sub-section highlighting the relevance of the respective component to the particular context of AI-mediated work. The article argues that there are no unsurmountable barriers in the Islamic tradition against the adoption of AI technologies in work sphere. However, important precautions should be considered to ensure that embracing AI will not be at the cost of work-related moral values. The article also highlights how important lessons can be learnt from the positive historical experience of automata that thrived in the Islamic civilization. (shrink)

The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good (...) reason to believe that it is very unlikely, the mere possibility of humanity causing extreme suffering to an AI is important enough to warrant serious consideration. This paper starts from the observation that both concerns rely on problematic philosophical assumptions. Rather than tackling these assumptions directly, it proceeds to present an argument that if one takes these assumptions seriously, then one has a moral obligation to advocate for a ban on the development of a conscious AI. (shrink)

Artificial Intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but frequently fail to suffice due (...) to the context sensitivity of ethical challenges. Frequently, additional (ethical) values emerge for specific applications, as e.g. challenges with fraud detection are very different from those around language technologies. Second, methods to tackle these challenges are discussed. Main ethical theories (virtue ethics, consequentialism, and deontology) are shown to provide a starting point, but often lack the details needed for actionable AI Ethics. Instead, mid-level philosophical theories coupled to design-approaches such as Design for Values, together with interdisciplinary working methods, offer the best way forwards. The chapter aims to show how these approaches can lead to an ethics of AI that is actionable and that can be proactively integrated in the design of AI systems. (shrink)

This paper explores artificial intelligence (AI) ethics from an Islamic perspective at a critical time for AI ethical norm-setting. It advocates for a pluralist approach to ethical AI benchmarking. As rapid advancements in AI technologies pose challenges surrounding autonomy, privacy, fairness, and transparency, the prevailing ethical discourse has been predominantly Western or Eurocentric. To address this imbalance, this paper delves into the Islamic ethical traditions to develop a framework that contributes to the global debate on optimal norm setting (...) for designing and using AI technologies.The paper outlines Islamic parameters for ethical values and moral actions in the context of AI's ethical uncertainties. It emphasizes the significance of both textual and non-textual Islamic sources in addressing these uncertainties while placing a strong emphasis on the notion of "good" or "maṣlaḥa" as a normative guide for AI's ethical evaluation. Defining maṣlaḥa as an ethical state of affairs in harmony with divine will, the paper highlights the coexistence of two interpretations of maṣlaḥa: welfarist/utility-based and duty-based. Islamic jurisprudence allows for arguments supporting ethical choices that prioritize building the technical infrastructure for AI to maximize utility. Conversely, it also supports choices that reject consequential utility calculations as the sole measure of value in determining ethical responses to AI advancements. (shrink)

Artificial intelligence can be a game changer to address the global challenge of humanity-threatening climate change by fostering sustainable development. Since chemical research and development lay the foundation for innovative products and solutions, this study presents a novel chemical research and development process backed with artificial intelligence and guiding ethical principles to account for both process- and outcome-related sustainability. Particularly in ethically salient contexts, ethical principles have to accompany research and development powered by artificial (...) class='Hi'>intelligence to promote social and environmental good and sustainability while preventing any harm for all stakeholders affected. (shrink)

Artificial Intelligence (AI) is a high-profile subject these days. In its brief history it has undergone several highs and lows and suffered from significant degrees of hype as well as antagonism and fear. One thing is clear: we are no closer to the goal of producing a truly sentient being than when it started. Nonetheless, the tools developed by AI researchers are here to stay and as with all technological advances it has its good and bad aspects. (...) In this article I will present a brief overview of the field of AI looking at what it is, how it developed, what are its specialism as well as some of its well-publicised successes, and failures, as well as pointing out some key Christian participants in the story. (shrink)

Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should (...) be good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)

Artificial Intelligence technology presents a multitude of ethical concerns, many of which are being actively considered by organizations ranging from small groups in civil society to large corporations and governments. However, it also presents ethical concerns which are not being actively considered. This paper presents a broad overview of twelve topics in ethics in AI, including function, transparency, evil use, good use, bias, unemployment, socio-economic inequality, moral automation and human de-skilling, robot consciousness and rights, dependency, social-psychological effects, (...) and spiritual effects. Each of these topics will be given a brief discussion, though each deserves much deeper consideration. (shrink)

The term ‘artificial intelligence’ has arguably come to function in political discourse as, what Laclau called, an ‘empty signifier’. This article traces the shifting political discourse on AI within three key institutions of global governance–OHCHR, WHO, and UNESCO–and, in so doing, highlights the role of ‘crisis’ moments in justifying a series of pivotal re-articulations. Most important has been the attachment of AI to the narrative around digital automation in human healthcare. Greatly enabled by the societal context of the (...) pandemic, all three institutions have moved from being critical of the unequal power relations in the economy of AI to, today, reframing themselves primarily as facilitators tasked with helping to ensure the application of AI technologies. The analysis identifies a shift in which human health and healthcare is framed as in a ‘crisis’ to which AI technology is presented as the remedy. The article argues the need to trace these discursive shifts as a means by which to understand, monitor, and where necessary also hold to account these changes in the governance of AI in society. (shrink)

The General Data Protection Regulation is a European Union regulation that will replace the existing Data Protection Directive on 25 May 2018. The most significant change is a huge increase in the maximum fine that can be levied for breaches of the regulation. Yet fewer than half of UK companies are fully aware of GDPR—and a number of those who were preparing for it stopped doing so when the Brexit vote was announced. A last-minute rush to become compliant is therefore (...) expected, and numerous companies are starting to offer advice, checklists and consultancy on how to comply with GDPR. In such an environment, artificial intelligence technologies ought to be able to assist by providing best advice; asking all and only the relevant questions; monitoring activities; and carrying out assessments. The paper considers four areas of GDPR compliance where rule based technologies and/or machine learning techniques may be relevant: Following compliance checklists and codes of conduct; Supporting risk assessments; Complying with the new regulations regarding technologies that perform automatic profiling; Complying with the new regulations concerning recognising and reporting breaches of security. It concludes that AI technology can support each of these four areas. The requirements that GDPR state for explanation and justification of reasoning imply that rule-based approaches are likely to be more helpful than machine learning approaches. However, there may be good business reasons to take a different approach in some circumstances. (shrink)

This article is based on a roundtable held at the Association of Internet Researchers annual conference in 2017, in Tartu, Estonia. The roundtable was organized by the Oxford Internet Institute’s Digital Ethics Lab. It was entitled “Artificial Intelligence and the Good Society”. It brought together four scholars—Michael Zimmer, Stine Lomborg, Ben Zevenbergen, and Corinne Cath—to discuss the promises and perils of artificial intelligence, in particular what ethical frameworks are needed to guide AI’s rapid development and (...) increased use in societies. The paper covers three case studies. They give a distinct overview of the ethical issues raised by the use of AI at different levels of analysis: top-down application of AI, bottom-up use of AI, and how academics and governments have reacted to these new challenges. From the case studies, four areas emerged. They represent some of the most topical ethical questions related to AI: its uses, its users, its designers, and the data that fuel it. Each of them provided a specific subset of ethical concerns that need further investigation. In conclusion, three recommendations are formulated for researchers and regulators to ensure the AI has a net-positive impact on society. (shrink)

For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both (...) the sets of options we choose from and the way those options are framed. Moreover, artificial intelligence and machine learning (AI/ML) makes it possible for those options and their framings—the choice architectures—to be tailored to the individual chooser. They are constructed based on information collected about our individual preferences, interests, aspirations, and vulnerabilities, with the goal of influencing our decisions. At the same time, because we are habituated to these technologies we pay them little notice. They are, as philosophers of technology put it, transparent to us—effectively invisible. I argue that this invisible layer of technological mediation, which structures and influences our decision-making, renders us deeply susceptible to manipulation. Absent a guarantee that these technologies are not being used to manipulate and exploit, individuals will have little reason to trust them. (shrink)

Artificial intelligence is becoming part of our everyday experience and is expected to be ever more integrated into ordinary life for many years to come. Thus, it is important for those in product development, research, and public policy to understand how the public’s perception of AI is shaped. In this study, we conducted focus groups and an online survey to determine the knowledge of AI held by the American public, and to judge whether entertainment media is a major (...) influence on how Americans perceive AI. What we found is that the American public’s knowledge of AI is patchy: some have a good understanding of what is and what is not AI, but many do not. When it came to understanding what AI can do, most respondents believe that AI could “replace human jobs” but few thought that it could “feel emotion.” Most respondents were optimistic about the future and impact of AI, though about one third were not sure. Most respondents also did not think they could develop an emotional bond with or be comfortable being provided care by an AI. Regarding the influence of entertainment media on perceptions of AI, we found a significant relationship between people’s beliefs about AI in entertainment media and their beliefs about AI in reality. Those who believe AI is realistically depicted in entertainment media were more likely to see AIs as potential emotional partners or apocalyptic robots than to imagine AIs taking over jobs or operating as surveillance tools. (shrink)

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more (...) complicated, going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals. (shrink)

Traditional notions of crime control often position the police against an individual, known or not yet known, who is responsible for the commission of a crime. However, with increasingly sophisticated technology, policing increasingly prioritizes the prevention of crime, making it necessary to ascertain who, or what class of persons, may be the next likely criminal before a crime can be committed, termed predictive policing. This causes a shift from individualized suspicion toward predictive profiling that may sway the expectations of a (...) police patrol. Classically, where a patrol officer forms reasonable suspicion prior to a stop, it is based upon his/her analysis of the situation taken as a whole in context. However, where a predictive profile is employed, information available to the officer accordingly adjusts his/her perception of context and affects the application of the reasonable suspicion standard. This article addresses the way in which new approaches to forming reasonable suspicion affect the due process protection of individuals’ fundamental rights. It argues that while an officer still operates with good faith discretion, using predictive profiling causes reasonable suspicion to be based on an augmented understanding of reality and as a result, due process guarantees are weakened. The rights to non-discrimination and the presumption of innocence are assessed and argued as illustrative of this weakening and shift in policing standards. The article ultimately argues that while predictive policing cannot be categorically labeled as inconsistent with criminal justice, changes as seemingly moderate as the manner in which discretion operates have larger effects on individuals’ rights. (shrink)

This introduction sets out the aims and scope of the Special Issue and provides an overview of each of the research articles and commentaries that follow.

The field of artificial intelligence (AI) can be characterized as the investigation of computational systems that exhibit intelligent behavior (including algorithms and models used in these systems). The emphasis is not so much on understanding (human) cognitive processes as on producing models, algorithms, and systems that are capable of apparently intelligent behavior by whatever means available. The idea of AI has had a long history that can be traced all the way back to, for example, Leibniz. The idea (...) was furthered through the development, early in this century, of mathematical logic, cybernetics, and information theory, all of which contributed to AI theorizing. Alan Turing's bold claim that machines can think, advanced in his famous 1950 paper, and the “Turing test” he proposed therein as a means to test the intelligence of machines predated and prompted the formation of AI. Six years later, the Dartmouth conference in 1956, the participants of which included Allen Newell, Herbert Simon, Marvin Minsky, and John McCarthy, inaugurated AI as an academic discipline. This contemporary version of AI was facilitated by the invention and rapidly widening use of digital computers. AI embodies an extremely diverse set of ideas that are often mutually conflicting (as in the case of symbolism versus connectionism), and its relationship with other fields studying cognition (such as psychology and linguistics) has been a close but uneasy one. (shrink)

PurposeThis paper aims to demonstrate the relevance of worldviews of the global south to debates of artificial intelligence, enhancing the human rights debate on artificial intelligence (AI) and critically reviewing the paper of UNESCO Commission on the Ethics of Scientific Knowledge and Technology (COMEST) that preceded the drafting of the UNESCO guidelines on AI. Different value systems may lead to different choices in programming and application of AI. Programming languages may acerbate existing biases as a people’s (...) worldview is captured in its language. What are the implications for AI when seen from a collective ontology? Ubuntu (I am a person through other persons) starts from collective morals rather than individual ethics.Design/methodology/approachLiterature overview on the African philosophy of Ubuntu as applied to artificial intelligence. Application of it to the United Nations Educational, Scientific and Cultural Organisation (UNESCO) debates on establishing guidelines to the ethics of artificial intelligence.FindingsMetaphysically, Ubuntu and its conception of social personhood (attained during one’s life) largely rejects transhumanism. When confronted with economic choices, Ubuntu favors sharing above competition and thus an anticapitalist logic of equitable distribution of AI benefits, humaneness and nonexploitation. When confronted with issues of privacy, Ubuntu emphasizes transparency to group members, rather than individual privacy, yet it calls for stronger (group privacy) protection. In democratic terms, it promotes consensus decision-making over representative democracy. Certain applications of AI may be more controversial in Africa than in other parts of the world, like care for the elderly, that deserve the utmost respect and attention, and which builds moral personhood. At the same time, AI may be helpful, as care from the home and community is encouraged from an Ubuntu perspective. The report on AI and ethics of the UNESCO World COMEST formulated principles as input, which are analyzed from the African ontological point of view. COMEST departs from “universal” concepts of individual human rights, sustainability and good governance which are not necessarily fully compatible with relatedness, including future and past generations. Next to rules based approaches, which may hamper diversity, bottom-up approaches are needed with intercultural deep learning algorithms.Research limitations/implicationsThere is very few existing literature on AI and Ubuntu. Therefore, this paper is of an explorative nature.Practical implicationsThe ethics of Ubuntu offers unique vantage points in looking at the organization of society and economics today, which are also relevant for development of AI, especially in its tenet of relatedness rather than individuality (and practical use of AI for individuals), taking responsibility for society as a whole (such as analyzing the benefit of AI for all strata of society), and embodying true inclusiveness. Whether looking at top-down guidelines for the development and implementation of AI or the bottom-up ethical learning process of AI (deep learning), ethics of the Global South can have an important role to play to combat global individualist tendencies and inequity, likely reinforced by AI. This warrants far more research.Social implicationsApplications of AI in Africa are not contextualized, do not address the most pressing needs of the African continent, lead to cybersecurity issues and also do not incorporate African ethics. UNESCO’s work in this regard is important but expert inputs are largely centered around Western “universal” principles and Organisation for Economic Cooperation and Development and EU precedence. African ethics have, so far, a small role to play in global ethics and philosophy and therefore risk to be overlooked in the discussion on AI and ethics. This is why the consultation process of UNESCO on ethics of AI was of paramount importance. However, it does not automatically lead to consultation of African philosophers or sages, as many are educated in Western (ized) education systems. See further details under practical implications.Originality/valueThis is a new area of research in which little work has been done so far. This paper offers the opportunity to widen the debate on AI and ethics beyond the conventional discourse, involving multiple worldviews, of which Ubuntu is just one. (shrink)

Puritanism may evolve into a technological variant based on norms of delegation of actions and perceptions to artificial intelligence. Instead of training self-control, people may be expected to cede their agency to self-controlled machines. The cost–benefit balance of this machine puritanism may be less aversive to wealthy individualistic democracies than the old puritanism they have abandoned.

During the 1950s, there was a burst of enthusiasm about whether artificial intelligence might surpass human intelligence. Since then, technology has changed society so dramatically that the focus of study has shifted toward society’s ability to adapt to technological change. Technology and rapid communications weaken the capacity of society to integrate into the broader social structure those people who have had little or no access to education. (Most of the recent use of communications by the excluded has (...) been disruptive, not integrative.) Interweaving of socioeconomic activity and large-scale systems had a dehumanizing effect on people excluded from social participation by these trends. Jobs vanish at an accelerating rate. Marketing creates demand for goods which stress the global environment, even while the global environment no longer yields readily accessible resources. Mining and petroleum firms push into ever more challenging environments (e.g., deep mines and seabed mining) to meet resource demands. These activities are expensive, and resource prices rise rapidly, further excluding groups that cannot pay for these resources. The impact of large-scale systems on society leads to mass idleness, with the accompanying threat of violent reaction as unemployed masses seek to blame both people in power as well as the broader social structure for their plight. Perhaps, the impact of large-scale systems on society has already eroded essential qualities of humanness. Humans, when they feel “socially useless,” are dehumanized. (At the same time, machines (at any scale) seem incapable of emotion or empathy.) Has the cost of technological progress been too high to pay? These issues are addressed in this paper. (shrink)

Determining the agency-status of machines and AI has never been more pressing. As we progress into a future where humans and machines more closely co-exist, understanding hallmark features of agency affords us the ability to develop policy and narratives which cater to both humans and machines. This paper maintains that decision-making processes largely underpin agential action, and that in most instances, these processes yield good results in terms of making good choices. However, in some instances, when faced with (...) two (or more) choices, an agent may find themselves with equal reasons to choose either - thus being presented with a tie. This paper argues that in the event of a tie, the ability to create a voluntarist reason is a hallmark feature of agency, and second, that AI, through current tie-breaking mechanisms does not have this ability, and thus fails at this particular feature of agency. (shrink)