This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors (...) or collections of data points. The imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)

The development of artificial intelligence (AI) in medicine raises fundamental ethical issues. As one example, AI systems in the field of mental health successfully detect signs of mental disorders, such as depression, by using data from social media. These AI depression detectors (AIDDs) identify users who are at risk of depression prior to any contact with the healthcare system. The article focuses on the ethical implications of AIDDs regarding affected users’ health-related autonomy. Firstly, it presents the (ethical) (...) discussion of AI in medicine and, specifically, in mental health. Secondly, two models of AIDDs using social media data and different usage scenarios are introduced. Thirdly, the concept of patient autonomy, according to Beauchamp and Childress, is critically discussed. Since this concept does not encompass the specific challenges linked with the digital context of AIDDs in social media sufficiently, the current analysis suggests, finally, an extended concept of health-related digital autonomy. (shrink)

The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people’s use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to examine public knowledge and behavior. This paper explores the ethical considerations of using artificial intelligence to monitor social media to (...) understand the public’s perspectives and behaviors surrounding COVID-19, including potential risks and benefits of an AI-driven approach. Importantly, investigators and ethics committees have a role in ensuring that researchers adhere to ethical principles of respect for persons, beneficence, and justice in a way that moves science forward while ensuring public safety and confidence in the process. (shrink)

The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not (...) yet been sufficiently explored. Our paper aims to fill this gap and address the AI ethics principle of fairness from a conceptual standpoint, drawing insights from accounts of fairness elaborated in moral philosophy and using them to conceptualise fairness as an ethical value and to redefine fairness in HMLA accordingly. To achieve our goal, following a first section aimed at clarifying the background, methodology and structure of the paper, in the second section, we provide an overview of the discussion of the AI ethics principle of fairness in HMLA and show that the concept of fairness underlying this debate is framed in purely distributive terms and overlaps with non-discrimination, which is defined in turn as the absence of biases. After showing that this framing is inadequate, in the third section, we pursue an ethical inquiry into the concept of fairness and argue that fairness ought to be conceived of as an ethical value. Following a clarification of the relationship between fairness and non-discrimination, we show that the two do not overlap and that fairness requires much more than just non-discrimination. Moreover, we highlight that fairness not only has a distributive but also a socio-relational dimension. Finally, we pinpoint the constitutive components of fairness. In doing so, we base our arguments on a renewed reflection on the concept of respect, which goes beyond the idea of equal respect to include respect for individual persons. In the fourth section, we analyse the implications of our conceptual redefinition of fairness as an ethical value in the discussion of fairness in HMLA. Here, we claim that fairness requires more than non-discrimination and the absence of biases as well as more than just distribution; it needs to ensure that HMLA respects persons both as persons and as particular individuals. Finally, in the fifth section, we sketch some broader implications and show how our inquiry can contribute to making HMLA and, more generally, AI promote the social good and a fairer society. (shrink)

This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and (...) applied in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making. (shrink)

The extended mind thesis maintains that the functional contributions of tools and artefacts can become so essential for our cognition that they can be constitutive parts of our minds. In other words, our tools can be on a par with our brains: our minds and cognitive processes can literally ‘extend’ into the tools. Several extended mind theorists have argued that this ‘extended’ view of the mind offers unique insights into how we understand, assess, and treat certain cognitive conditions. In this (...) chapter we suggest that using AI extenders, i.e., tightly coupled cognitive extenders that are imbued with machine learning and other ‘artificially intelligent’ tools, presents both new ethical challenges and opportunities for mental health. We focus on several mental health conditions that can develop differently by the use of AI extenders for people with cognitive disorders and then discuss some of the related opportunities and challenges. (shrink)

Artificial Intelligence (AI), especially of the machine learning (ML) variety, is used by health care organizations to assist with a number of tasks, including diagnosing patients and optimizing operational workflows. AI products already proliferate the health care market, with usage increasing as the technology matures. Although AI may potentially revolutionize health care, the use of AI in health settings also leads to risks ranging from violating patient privacy to implementing a biased algorithm. This chapter begins with a broad (...) overview of health care AI and how it is currently used. We then adopt a “lifecycle” approach to discussing issues with health care AI. We start by discussing the legal and ethical issues pertaining to how data to build AI are gathered in health care settings, focusing on privacy. Next, we turn to issues in algorithm development, especially algorithmic bias. We then discuss AI deployment to treat patients, focusing on informed consent. Finally, we will discuss existing oversight mechanisms for health AI in the United States: liability and regulation. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering (...) the roles of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

Applications of artificial intelligence/machine learning in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ml for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ml introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and (...) critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ml for health care. We suggest that AI/ml technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ml for health care and conclude with suggestions for addressing these practical and conceptual challenges. (shrink)

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made (...) and used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn. (shrink)

The potential for artificial intelligences and robotics in achieving the capacity of consciousness, sentience and rationality offers the prospect that these agents have minds. If so, then there may be a potential for these minds to become dysfunctional, or for artificial intelligences and robots to suffer from mental illness. The existence of artificially intelligent psychopathology can be interpreted through the philosophical perspectives of mental illness. This offers new insights into what it means to have either robot (...) or human mental disorders, but may also offer a platform on which to examine the mechanisms of biological or artificially intelligent psychiatric disease. The possibility of mental illnesses occurring in artificially intelligent individuals necessitates the consideration that at some level, they may have achieved a mental capability of consciousness, sentience and rationality such that they can subsequently become dysfunctional. The deeper philosophical understanding of these conditions in mankind and artificial intelligences might therefore offer reciprocal insights into mental health and mechanisms that may lead to the prevention of mental dysfunction. (shrink)

The research ethics framework put forward by McCradden et al. to support systematic inquiry in the development of artificial intelligence and machine learning technologies in healt...

A concise but informative overview of AI ethics and policy. -/- Artificial intelligence, or AI for short, has generated a staggering amount of hype in the past several years. Is it the game-changer it's been cracked up to be? If so, how is it changing the game? How is it likely to affect us as customers, tenants, aspiring homeowners, students, educators, patients, clients, prison inmates, members of ethnic and sexual minorities, and voters in liberal democracies? Authored by experts (...) in fields ranging from computer science and law to philosophy and cognitive science, this book offers a concise overview of moral, political, legal and economic implications of AI. It covers the basics of AI's latest permutation, machine learning, and considers issues such as transparency, bias, liability, privacy, and regulation. -/- Both business and government have integrated algorithmic decision support systems into their daily operations, and the book explores the implications for our lives as citizens. For example, do we take it on faith that a machine knows best in approving a patient's health insurance claim or a defendant's request for bail? What is the potential for manipulation by targeted political ads? How can the processes behind these technically sophisticated tools ever be transparent? The book discusses such issues as statistical definitions of fairness, legal and moral responsibility, the role of humans in machine learning decision systems, “nudging” algorithms and anonymized data, the effect of automation on the workplace, and AI as both regulatory tool and target. (shrink)

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

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI (...) safety; and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

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

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social (...) explainability can require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the wide- spread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning. (shrink)

The ethics of artificial intelligence and machine learning exemplify the conceptual struggle between applying familiar pathways of ethical analysis versus generating novel strategies. Mel...

Common mental health disorders are rising globally, creating a strain on public healthcare systems. This has led to a renewed interest in the role that digital technologies may have for improving mental health outcomes. One result of this interest is the development and use of artificial intelligence for assessing, diagnosing, and treating mental health issues, which we refer to as ‘digital psychiatry’. This article focuses on the increasing use of digital psychiatry outside of clinical settings, in (...) the following sectors: education, employment, financial services, social media, and the digital well-being industry. We analyse the ethical risks of deploying digital psychiatry in these sectors, emphasising key problems and opportunities for public health, and offer recommendations for protecting and promoting public health and well-being in information societies. (shrink)

This paper presents work in progress on artificial intelligence in medicine (AIM) within the larger context of cognitive science. It introduces and develops the notion ofemergence both as an inevitable evolution of artificial intelligence towards machine learning programs and as the result of a synergistic co-operation between the physician and the computer. From this perspective, the emergence of knowledge takes placein fine in the expert's mind and is enhanced both by computerised strategies of induction and deduction, (...) and by software abilities to dialogue, co-operate and function as a cognitive extension of the physician's intellectual capabilities. The proposed methodology gives the expert a prominent role which consists, first, of faithfully enunciating the descriptive features of his medical knowledge, thus giving the computer a precise description of his own perception of basic medicine, and secondly, of painstakingly gathering patients into computerised case bases which simulate exhaustive long-term memory. The AI capacities for knowledge elaboration are then triggered, giving rise to mathematically optimal diagnoses, prognoses, or treatment protocols which the physician may then evaluate, accept, reject, or adapt at his convenience, and finally append to a knowledge base. The idea of emergence embraces many issues concerning the purpose and intent of artificial intelligence in medical practice. Particularly, we address the representation problem as it is raised by classical decisional knowledge-based systems, and develop the notions of classifiers and hybrid systems as possible answers to this problem. Finally, since the whole methodology touches the problem of technological investment in health care, now inherent in modern medical practice, some ethical considerations accompany the exposé. (shrink)

Well-being is an important policy concept including in discussions around the use of artificial intelligence, machine learning and robotics. Disabled people experience challenges in their well-being. Therefore, the aim of our scoping review study of academic abstracts employing Scopus, IEEE Xplore, Compendex and the 70 databases from EBSCO-HOST as sources was to better understand how academic literature focusing on AI/ML/robotics engages with well-being in relation to disabled people. Our objective was to answer the following research question: how and (...) to what extent does the AI/ML/robot literature we covered include well-being in relation to disabled people? We found 2071 academic abstracts covering AI/ML and well-being, and 1055 covering robotics and well-being. Within these abstracts, only 39 covered AI/ML and 48 robotics and well-being in relation to disabled people. The tone of the coverage was techno-positive and techno-optimistic arguing that AI/ML/robotics could improve the well-being of disabled people in general or improve well-being by helping disabled people overcome their ‘disability’ or make tasks easier. No negative effects that AI/ML/robotics could have or has had on the well-being of disabled people were mentioned. Disabled people were portrayed only within patient, client, or user roles but not in their roles as stakeholders in the governance of AI/ML/robotics discussions. This biased and limited coverage of the impact of AI/ML/robotics on the well-being of disabled people disempowers disabled people. (shrink)

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

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative (...) supervised learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

The rapid adoption and implementation of artificial intelligence in medicine creates an ontologically distinct situation from prior care models. There are both potential advantages and disadvantages with such technology in advancing the interests of patients, with resultant ontological and epistemic concerns for physicians and patients relating to the instatiation of AI as a dependent, semi- or fully-autonomous agent in the encounter. The concept of libertarian paternalism potentially exercised by AI (and those who control it) has created challenges to conventional (...) assessments of patient and physician autonomy. The unclear legal relationship between AI and its users cannot be settled presently, an progress in AI and its implementation in patient care will necessitate an iterative discourse to preserve humanitarian concerns in future models of care. This paper proposes that physicians should neither uncritically accept nor unreasonably resist developments in AI but must actively engage and contribute to the discourse, since AI will affect their roles and the nature of their work. One’s moral imaginative capacity must be engaged in the questions of beneficence, autonomy, and justice of AI and whether its integration in healthcare has the potential to augment or interfere with the ends of medical practice. (shrink)

Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, apply primarily to Europe, the USA, China and other technologically developed nations. Africa is yet to leverage the potential applications of AI and ML within the medical space. Sub-Saharan African countries are currently disadvantaged economically and infrastructure-wise. Yet precisely, these circumstances create significant opportunities for (...) the deployment of medical AI, which has already been deployed in some places in the continent. However, while AI and ML have come with enormous promises in Africa, there are still challenges when it comes to successfully applying AI and ML designed elsewhere within the African context, especially in diagnosing mental health disorders. We argue, in this paper, that there ought not to be a homogeneous/generic design of AI and ML used in diagnosing mental health disorders. Our claim is grounded on the premise that mental health disorders cannot be diagnosed solely on ‘factual evidence’ but on both factual evidence and value-laden judgements of what constitutes mental health disorders in sub-Saharan Africa. For ML to play a successful role in diagnosing mental health disorders in sub-Saharan African medical spaces, with a precise focus on South Africa, we allude that it ought to understand what sub-Saharan Africans consider as mental health disorders, that is, the value-laden judgements of some conditions. (shrink)

This open access collection of AI ethics case studies is the first book to present real-life case studies combined with commentaries and strategies for overcoming ethical challenges. Case studies are one of the best ways to learn about ethical dilemmas and to achieve insights into various complexities and stakeholder perspectives. Given the omnipresence of AI ethics in academic, policy and media debates, the book will be suitable for a wide range of audiences, from scholars of different disciplines (e.g. (...) AI science, ethics, politics, philosophy, economics) to policy-makers, lobbying NGOs, teachers and the educated public. (shrink)

Digital phenotyping is the term given to the capturing and use of user log data from health and wellbeing technologies used in apps and cloud-based services. This paper explores ethical issues in making use of digital phenotype data in the arena of digital health interventions. Products and services based on digital wellbeing technologies typically include mobile device apps as well as browser-based apps to a lesser extent, and can include telephony-based services, text-based chatbots, and voice-activated chatbots. Many of these digital (...) products and services are simultaneously available across many channels in order to maximize availability for users. Digital wellbeing technologies offer useful methods for real-time data capture of the interactions of users with the products and services. It is possible to design what data are recorded, how and where it may be stored, and, crucially, how it can be analyzed to reveal individual or collective usage patterns. The paper also examines digital phenotyping workflows, before enumerating the ethical concerns pertaining to different types of digital phenotype data, highlighting ethical considerations for collection, storage, and use of the data. A case study of a digital health app is used to illustrate the ethical issues. The case study explores the issues from a perspective of data prospecting and subsequent machine learning. The ethical use of machine learning and artificial intelligence on digital phenotype data and the broader issues in democratizing machine learning and artificial intelligence for digital phenotype data are then explored in detail. (shrink)

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by (...) their creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also. (shrink)

The application of artificial intelligence and machine learning technologies in healthcare have immense potential to improve the care of patients. While there are some emerging practices surro...

Digitalisation of health and the use of health data in artificial intelligence, and machine learning (ML), including for applications that will then in turn be used in healthcare are major themes permeating current UK and other countries’ healthcare systems and policies. Obtaining rich and representative data is key for robust ML development, and UK health data sets are particularly attractive sources for this. However, ensuring that such research and development is in the public interest, produces public benefit and (...) preserves privacy are key challenges. Trusted research environments (TREs) are positioned as a way of balancing the diverging interests in healthcare data research with privacy and public benefit. Using TRE data to train ML models presents various challenges to the balance previously struck between these societal interests, which have hitherto not been discussed in the literature. These challenges include the possibility of personal data being disclosed in ML models, the dynamic nature of ML models and how public benefit may be (re)conceived in this context. For ML research to be facilitated using UK health data, TREs and others involved in the UK health data policy ecosystem need to be aware of these issues and work to address them in order to continue to ensure a ‘safe’ health and care data environment that truly serves the public. (shrink)

Our fascination with artificial intelligence (AI), robots and sentient machines has a long history, and references to such humanoids are present even in ancient myths and folklore. The advancements in digital and computational technology have turned this fascination into apprehension, with the machines often being depicted as a binary to the human. However, the recent domains of academic enquiry such as transhumanism and posthumanism have produced many a literature in the genre of science fiction (SF) that endeavours to (...) alter this antagonistic notion of AI. In his novel Klara and the Sun, Kazuo Ishiguro explores this notion of AI as a caring machine capable of nursing an ailing young girl back to health. Through this portrayal of AI as a sentient being capable of empathy and cognition, Ishiguro is attempting to usher in a new perception of AI: a posthuman perception that challenges the conventional notions of AI as a machine devoid of emotions. The novel further expands on the idea of self, soul and human consciousness and ponders on the question, what makes humans human, and if it is possible to imbibe these qualities onto an AI. Owing to the novelty of this notion, an unexplored avenue that deserves further exploration, this paper examines the plausibility of this new notion of AI through a careful exegesis of the novel. The paper also attempts to chart the impact of SF in society and culture. The study reveals a positive shift in perception towards AI, and there seems to be much scope for interdisciplinary and transdisciplinary research. (shrink)

Rapid advancements in artificial intelligence and machine learning (AI/ML) in healthcare raise pressing questions about how much users should trust AI/ML systems, particularly for high stakes clinical decision-making. Ensuring that user trust is properly calibrated to a tool’s computational capacities and limitations has both practical and ethical implications, given that overtrust or undertrust can influence over-reliance or under-reliance on algorithmic tools, with significant implications for patient safety and health outcomes. It is, thus, important to better understand how variability (...) in trust criteria across stakeholders, settings, tools and use cases may influence approaches to using AI/ML tools in real settings. As part of a 5-year, multi-institutional Agency for Health Care Research and Quality-funded study, we identify trust criteria for a survival prediction algorithm intended to support clinical decision-making for left ventricular assist device therapy, using semistructured interviews (n=40) with patients and physicians, analysed via thematic analysis. Findings suggest that physicians and patients share similar empirical considerations for trust, which were primarilyepistemicin nature, focused on accuracy and validity of AI/ML estimates. Trust evaluations considered the nature, integrity and relevance of training data rather than the computational nature of algorithms themselves, suggesting a need to distinguish ‘source’ from ‘functional’ explainability. To a lesser extent, trust criteria were also relational (endorsement from others) and sometimes based on personal beliefs and experience. We discuss implications for promoting appropriate and responsible trust calibration for clinical decision-making use AI/ML. (shrink)

The ethical review of research using methods of artificial intelligence and machine learning in health care contexts has become an important challenge for Research Ethics Boards (also refer...

Despite the success of artificial intelligence, we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive (...) models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand. (shrink)

The advent of the intelligent robot has occupied a significant position in society over the past decades and has given rise to new issues in society. As we know, the primary aim of artificial intelligence or robotic research is not only to develop advanced programs to solve our problems but also to reproduce mental qualities in machines. The critical claim of artificial intelligence advocates is that there is no distinction between mind and machines and thus they argue (...) that there are possibilities for machine ethics, just as human ethics. Unlike computer ethics, which has traditionally focused on ethical issues surrounding human use of machines, AI or machine ethics is concerned with the behaviour of machines towards human users and perhaps other machines as well, and the ethicality of these interactions. The ultimate goal of machine ethics, according to the AI scientists, is to create a machine that itself follows an ideal ethical principle or a set of principles; that is to say, it is guided by this principle or these principles in decisions it makes about possible courses of action it could takea. Thus, machine ethics task of ensuring ethical behaviour of an artificial agent. Although, there are many philosophical issues related to artificial intelligence, but our attempt in this paper is to discuss, first, whether ethics is the sort of thing that can be computed. Second, if we are ascribing mind to machines, it gives rise to ethical issues regarding machines. And if we are not drawing the difference between mind and machines, we are not only redefining specifically human mind but also the society as a whole. Having a mind is, among other things, having the capacity to make voluntary decisions and actions. The notion of mind is central to our ethical thinking, and this is because the human mind is self-conscious, and this is a property that machines lack, as yet. (shrink)

Certain research strands can yield “forbidden knowledge”. This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or (...) text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications. (shrink)

Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years—almost 11,000 papers in total. Our statistical approach focuses on conflicts of interest, innovation, and gender equality. We have obtained four (...) main findings. (1) Academic–corporate collaborations are growing in numbers. At the same time, we found that conflicts of interest are rarely disclosed. (2) Industry papers amply mention terms that relate to particular trending machine learning topics earlier than academia does. (3) Industry papers are not lagging behind academic papers with regard to how often they mention keywords that are proxies for social impact considerations. (4) Finally, we demonstrate that industry papers fall short of their academic counterparts with respect to the ratio of gender diversity. We believe that this work is a starting point for an informed debate within and outside of the ML community. (shrink)

Headlines in 2019 are inundated with claims about the “digital society,” making sweeping assertions of societal benefits and dangers caused by a range of technologies. This situation would seem an ideal motivation for ethics research, and indeed much research on this topic is published, with more every day. However, ethics researchers may feel a sense of déjà vu, as they recall decades of other heavily promoted technological platforms, from genomics and nanotechnology to machine learning. How should (...) class='Hi'>ethics researchers respond to the waves of rhetoric and accompanying academic and policy-oriented research? What makes the digital society significant for ethics research? In this paper, we consider two examples of digital technologies (artificial intelligence and neural technologies), showing the pattern of societal and academic resources dedicated to them. This pattern, we argue, reveals the jointly sociological and ethical character of significance attributed to emerging technologies. By attending to insights from pragmatism and science and technology studies, ethics researchers can better understand how these features of significance affect their work and adjust their methods accordingly. In short, we argue that the significance driving ethics research should be grounded in public engagement, critical analysis of technology’s “vanguard visions,” and in a personal attitude of reflexivity. (shrink)

AI-driven decisions can draw data from virtually any area of your life to make a decision about virtually any other area of your life. That creates fairness issues. Effective regulation to ensure fairness requires that AI systems be transparent. That is, regulators must have sufficient access to the factors that explain and justify the decisions. One approach to transparency is to require that systems be explainable, as that concept is understood in computer science. A system is explainable if one (...) can provide a human-understandable explanation of why it makes any particular prediction. Explainability should not be equated with transparency. To address transparency and characterize its relation to explainability, we define transparency for a regulatory purpose. A system is transparent for a regulatory purpose (r-transparent) when and only when regulators have an explanation, adequate for that purpose, of why it yields the predictions it does. Explainability remains relevant to transparency but turns out to be neither necessary nor sufficient for it. The concepts of explainability and r-transparency combine to yield four possibilities: explainable and either r-transparent or not; and not explainable and either not r-transparent or r-transparent. Combining r-transparency with ideas from the Harvard computer scientist Cynthia Dwork, we propose four requirements on AI systems. (shrink)

The early examples of self-directing robots attracted the interest of both scientific and military communities. Biologists regarded these devices as material models of animal tropisms. Engineers envisaged the possibility of turning self-directing robots into new “intelligent” torpedoes during World War I. Starting from World War II, more extensive interactions developed between theoretical inquiry and applied military research on the subject of adaptive and intelligent machinery. Pioneers of Cybernetics were involved in the development of goal-seeking warfare devices. But collaboration occasionally turned (...) into open dissent. Founder of Cybernetics Norbert Wiener, in the aftermath of World War II, argued against military applications of learning machines, by drawing on epistemological appraisals of machine learning techniques. This connection between philosophy of science and techno-ethics is both strengthened and extended here. It is strengthened by an epistemological analysis of contemporary machine learning from examples; it is extended by a reflection on ceteris paribus conditions for models of adaptive behaviours. (shrink)

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent (...) advances in neurology (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society. (shrink)

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of (...) general ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? (shrink)

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Much recent literature examines the ethical consequences of employing ML within medical and psychiatric practice but the potential impact on psychiatric diagnostic systems has so far not been well-developed. In this article, we aim to explore the challenges that arise from the recent (...) use of ANNs for the old problems of psychiatric nosology. To enable an empirically supported critical reflection on the topic, we conducted semi-structured qualitative interviews with Swiss and German experts in computational psychiatry. Here, we report our findings structured around two themes, namely (1) the possibility of using ML for defining or refining of psychiatric classification, and (2) the desirability of employing ML for psychiatric nosology. We discuss these themes by relating them to recent debates about network theory for psychiatric nosology and show why empirical research in the field should critically reflect on its contribution to psychopathology research. In sum, we argue that beyond technical, regulatory, and ethical challenges, philosophical reflection is crucial to harness the potential of ML in psychiatry. (shrink)

Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. (...) Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements. (shrink)

Given the pervasiveness of AI systems and their potential negative effects on people’s lives (especially among already marginalised groups), it becomes imperative to comprehend what goes on when an AI system generates a result, and based on what reasons, it is achieved. There are consistent technical efforts for making systems more “explainable” by reducing their opaqueness and increasing their interpretability and explainability. In this paper, we explore an alternative non-technical approach towards explainability that complement existing ones. Leaving aside technical, statistical, (...) or data-related issues, we focus on the very conceptual underpinnings of the design decisions made by developers and other stakeholders during the lifecycle of a machine learning project. For instance, the design and development of an app to track snoring to detect possible health risks presuppose some picture or another of “health”, which is a key notion that conceptually underpins the project. We take it as a premise that these key concepts are necessarily present during design and development, albeit perhaps tacitly. We argue that by providing “justificatory explanations” about how the team understands the relevant key concepts behind its design decisions, interested parties could gain valuable insights and make better sense of the workings and outcomes of systems. Using the concept of “health”, we illustrate how a particular understanding of it might influence decisions during the design and development stages of a machine learning project, and how making this explicit by incorporating it into ex-post explanations might increase the explanatory and justificatory power of these explanations. We posit that a greater conceptual awareness of the key concepts that underpin design and development decisions may be beneficial to any attempt to develop explainability methods. We recommend that “justificatory explanations” are provided as technical documentation. These are declarative statements that contain at its simplest: (1) a high-level account of the understanding of the relevant key concepts a team possess related to a project’s main domain, (2) how these understandings drive decision-making during the life-cycle stages, and (3) it gives reasons (which could be implicit in the account) that the person or persons doing the explanation consider to have plausible justificatory power for the decisions that were made during the project. (shrink)

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent (...) advances in neurology (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society. (shrink)

The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to (...) their specification of preventing human harm, stipulating obedience to humans and incorporating robotic self-protection. However the overwhelming predominance in the study of this field has focussed on human–robot interactions without fully considering the ethical inevitability of future artificial intelligences communicating together and has not addressed the moral nature of robot–robot interactions. A new robotic law is proposed and termed AIonAI or artificial intelligence-on-artificial intelligence. This law tackles the overlooked area where future artificial intelligences will likely interact amongst themselves, potentially leading to exploitation. As such, they would benefit from adopting a universal law of rights to recognise inherent dignity and the inalienable rights of artificial intelligences. Such a consideration can help prevent exploitation and abuse of rational and sentient beings, but would also importantly reflect on our moral code of ethics and the humanity of our civilisation. (shrink)