Mariarosaria Taddeo

This article provides a methodology for the interpretation of AI ethics principles to specify ethical criteria for the development and deployment of AI systems in high-risk domains. The methodology consists of a three-step process deployed by an independent, multi-stakeholder ethics board to: (1) identify the appropriate level of abstraction for modelling the AI lifecycle; (2) interpret prescribed principles to extract specific requirements to be met at each step of the AI lifecycle; and (3) define the criteria to inform purpose- and context-specific balancing of the principles. The methodology presented in this article is designed to be agile, adaptable, and replicable, and when used as part of a pro-ethical institutional culture, will help to foster the ethical design, development, and deployment of AI systems. The application of the methodology is illustrated through reference to the UK Ministry of Defence AI ethics principles.

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain.

The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key normative choices and corresponding tradeoffs that are involved in specifying guidance for the implementation of AI ethics principles in the defence domain. These correspond to: the AI lifecycle model used; the scope of stakeholder involvement; the accountability goals chosen; the choice of auditing requirements; and the choice of mechanisms for transparency and traceability. We provide initial recommendations for navigating these tradeoffs and highlight the importance of a pro-ethical institutional culture.

Much of the hype about AI exploits the fact that human cognition, language, and categorization rely on imaginative scaffolding. Some degree of metaphor and conceptual borrowing should not be seen as a methodological failure. Law does not meet AI as a blank slate. It reaches for metaphors and other imaginative patterns that make the unfamiliar legible, channeling attention toward certain features and away from others. These are not superficial rhetorical devices, but imaginative patterns that do real work: They frame risks, allocate authority, and steer doctrinal development. The paper uses cognitive linguistics to trace how imaginative patterns structure the regulatory agenda and create path dependence. It concentrates on three especially influential conceptions that circulate across technical, legal, and public discourse: (artificial) intelligence, black box, and hallucinations. “Intelligence” invites personification and prototype effects that lead to misplaced assumptions about agency, even though what is actually being produced is best described as a simulation of a much narrower capacity associated with intelligence: linguistic competence. “Black box” turns a distributed sociotechnical system into a metaphoric bounded container, misleadingly focusing concerns about transparency and responsibility on the model abstracted from its development and implementation. “Hallucinations” imports from psycho-pathology an inappropriate frame which obscures the fact that many errors are foreseeable products of intentional choices. We suggest, instead, reframing them as deferential hazards that arise from design features and expected interactional dynamics.

Recommender Systems (RS) on digital platforms increasingly influence user behavior, raising ethical concerns, privacy risks, harmful content promotion, and diminished user autonomy. This article examines RS within the framework of regulations and lawsuits in the United States and advocates for legislation that can withstand constitutional scrutiny under First Amendment protections. We propose (re)framing RS-curated content as commercial speech, which is subject to lessened free speech protections. This approach provides a practical path for future legislation that would allow for effective oversight of RS, particularly in areas of substantial public interest like child safety, national security, and misinformation.

Battlefield data have become a critical asset in contemporary defence. Yet there is a gap in the relevant literature, whilst it addresses various aspects of defence data management—including cybersecurity, interoperability, and decision-making support—it overlooks how these data should be collected, curated, and accessed to enhance the responsible development of AI-enabled defence capabilities. This article addresses this gap first by reviewing existing data policies strategies of NATO and Five Eyes Member States to assess the extent to which they focus on battlefield data, and then by outlining how national defence organisations should manage these data to maximise their strategic value whilst mitigating the attendant ethical, legal, and social risks. We argue that due to their non-rivalrous, artificially excludable nature, battlefield data should be conceptualised as an artificial club good and that national defence organisations have ethical obligations to act as club manager to leverage the potential of these data to develop more robust, reliable and controllable AI defence capabilities. We conclude the analysis proposing two sets of policy recommendations to aid national defence organisations in discharging their responsibilities as club managers for battlefield data.

A compilation of cutting-edge, comprehensive insights into digital ethics from leading scholars As digital technologies shape every aspect of today's society, ethical considerations have never been more pressing. In A Companion to Digital Ethics, editors Luciano Floridi and Mariarosaria Taddeo bring together leading experts to analyse key ethical challenges posed by artificial intelligence, privacy, cybersecurity, cyberwarfare, sustainability, digital consent, and many other topics. With a multidisciplinary approach, this authoritative volume introduces all the relevant topics in digital ethics clearly and accessibly, integrating insights from philosophy, law, computer science, and the social sciences. Unlike other collections that focus on specialised research or introductory overviews, this Companion strikes a critical balance—offering a systematic, comprehensive, simple yet rigorous examination of digital ethics that is both conceptually rich and practically relevant. Each chapter can be read as part of the whole or independently. Throughout the book, the contributing authors equip the reader with diverse perspectives for navigating ethical dilemmas in the evolving digital landscape, with an essential list of references and further readings. Integrating real-world examples and case studies throughout, A Companion to Digital Ethics: Explores the ethical value and implications of digital technologies and their applications Offers clear and precise analysis of both current and potential ethical challenges Includes discussions of ethical issues in contexts of technology governance and digital law Helps professionals and policymakers implement ethical principles in AI and digital technologies Contains authoritative and cutting-edge chapters, edited by two leading experts in digital ethics Whether used in academic courses or professional discussions on responsible digital innovation, A Companion to Digital Ethics is an essential guide to understanding and addressing the ethical questions of the digital age. It is a must-have resource for undergraduate and postgraduate students in any academic discipline, as well as policymakers and practitioners seeking to implement ethical frameworks for emerging technologies.

The volume establishes an ethical framework for the identification, analysis, and resolution of ethical challenges that arise from the uses of artificial intelligence (AI) in defence, ranging from intelligence analysis to cyberwarfare and autonomous weapon systems. It does so with the goal of advancing the relevant debate and to inform the ethical governance of AI in defence. Centring on the autonomy and learning capabilities of AI technologies, the work is rooted in AI ethics and Just War Theory. It provides a systemic conceptual analysis of the different uses of AI in defence and their ethical implications, proposes ethical principles and a methodology for their implementation in practice. It then translates this analysis into actionable recommendations for decision-maker and policymakers to foster ethical governance of AI in the defence sector.

In this article we focus on the definition of autonomous weapons systems (AWS). We provide a comparative analysis of existing official definitions of AWS as provided by States and international organisations, like ICRC and NATO. The analysis highlights that the definitions draw focus on different aspects of AWS and hence lead to different approaches to address the ethical and legal problems of these weapon systems. This approach is detrimental both in terms of fostering an understanding of AWS and in facilitating agreement around conditions of deployment and regulations of their use and, indeed, whether AWS are to be used at all. We build on the comparative analysis to identify essential aspects of AWS and then offer a definition that provides a value-neutral ground to address the relevant ethical and legal problems. In particular, we identify four key aspects – autonomy; adapting capabilities of AWS; human control; and purpose of use – as the essential factors to define AWS and which are key when considering the related ethical and legal implications.

The article has the goal of indicating how to harness the potential for good of artificial intelligence (AI) – defined as a distinct form of autonomous and self- learning agency and thus raises unique ethical challenges – while mitigating its ethical challenges. The analyses focuses first on uses of AI that may lead to undue discrimination, lack of explainability, the responsibility gap, and the nudging potential of AI and its negative impact on human self-determination. It then turns on the role that ethical analyses in harnessing the potential for good of AI and argues that existing guidelines for the ethics design, development and use of AI will be effective insofar as they are translated into viable guidelines to shape AI-based innovation and that this is the task of digital ethics as a translational ethics.

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched (Scopus, Google Scholar, Philpapers, Web of Science, Pub Med), in April 2019, to support the following research question: “how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be ‘ethically mindful?’”. A series of screening stages were carried out—for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)—yielding a total of 156 papers that were included in the review.We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. This article contributes to the debate on AI in health care by offering a comprehensive analysis of the relevant literature, focusing on the ethical implications for individuals, interpersonal relationships, groups, institutions, societies and the health sector as a whole. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new ‘AI winter’ could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.

The idea of Artificial Intelligence for Social Good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good.

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become a world leader in AI by 2030, to monetise AI into a trillion-yuan ($150 billion) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.

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

As cyber attacks continue to escalate in terms of frequency, impact, and level of refinement so do the efforts of state actors to acquire new offensive capabilities to defend, counter or retaliate incoming attacks. Artificial Intelligence (AI) has become a key technology both for attacking and defending in cyberspace. When considered in the current regulatory vacuum this is problematic, as AI-enabled cyber conflict may escalate and threaten national security and international stability. This is why this article argues for the need to define regulation for state use of AI for defence purposes and calls on regional forums, such as NATO and the European Union, to revive efforts and prepare the ground for an initiative led by the United Nations. It concludes by offering three recommendations as key aspects to regulate. There are: legal boundaries that distinguish between legitimate and illegitimate targets and define proportionality of responses; promote testing strategies with allies to organize sparring exercises among allies to test AI-based defence tactics; monitor and enforce rules, a third-party authority with teeth, should rule on whether red lines, proportionality, responsible deployment or disclosure norms have been breached.

Artificial Intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime (AIC). AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space.

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2)). The golas are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

During the past decade, ethicists as well as policy-makers, political and social scientists, and experts in military studies have been concerned with the topic of cyber-conflicts and in particular the political and social ramifications for contemporary society. This book offers a clear and accessible introduction to the ethical analysis of cyber-conflicts, covering such topics as cyber-crime, cyber-terrorism, and cyber-warfare and cyber-activism. A useful introduction for students _The Ethics of Cyber-Conflicts _allows the readers to acquire simultaneously a comprehensive view of cyber conflicts as a new and pervasive phenomenon affecting contemporary information societies and a sophisticated, in depth understanding of the ethical problems that each instance of such phenomenon engender.

The digital revolution provides huge opportunities to improve private and public life, and our environments, from health care to smart cities and global warming. Unfortunately, such opportunities come with significant ethical challenges. In particular, the extensive use of increasingly more data—often personal, if not sensitive (Big Data)—the growing reliance on algorithms to analyse them in order to shape choices and to make decisions (including machine learning, AI, and robotics), and the gradual reduction of human involvement or oversight over many automatic processes, pose pressing questions about fairness, responsibility, and respect of human rights.

In this chapter, I analyse deterrence theory and argue that its applicability to cyberspace is limited and that these limits are not trivial. They are the consequence of fundamental differences between deterrence theory and the nature of cyber conflicts and cyberspace. The goals of this analysis are to identify the limits of deterrence theory in cyberspace, clear the ground of inadequate approaches to cyber deterrence, and define the conceptual space for a domain-specific theory of cyber deterrence, still to be developed.

In this chapter I propose an ethical analysis of information warfare, the warfare waged in the cyber domain. The goal is twofold, filling the theoretical vacuum surrounding this phenomenon and providing the conceptual grounding for the definition of new ethical regulations for information warfare. I argue that Just War Theory is a necessary but not sufficient instrument for considering the ethical implications of information warfare and that a suitable ethical analysis of this kind of warfare is developed when Just War Theory is merged with Information Ethics. In the initial part of the chapter, I describe information warfare and its main features and highlight the problems that arise when Just War Theory is endorsed as a means of addressing ethical problems engendered by this kind of warfare. In the final part, I introduce the main aspects of Information Ethics and define three principles for a just information warfare resulting from the integration of Just War Theory and Information Ethics.

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain.

Recommender Systems (RS) on digital platforms increasingly influence user behavior, raising ethical concerns, privacy risks, harmful content promotion, and diminished user autonomy. This article examines RS within the framework of regulations and lawsuits in the United States and advocates for legislation that can withstand constitutional scrutiny under First Amendment protections. We propose (re)framing RS-curated content as commercial speech, which is subject to lessened free speech protections. This approach provides a practical path for future legislation that would allow for effective oversight of RS, particularly in areas of substantial public interest like child safety, national security, and misinformation.

This article presents the first thematic review of the literature on the ethical issues concerning digital well-being. The term ‘digital well-being’ is used to refer to the impact of digital technologies on what it means to live a life that is good for a human being. The review explores the existing literature on the ethics of digital well-being, with the goal of mapping the current debate and identifying open questions for future research. The review identifies major issues related to several key social domains: healthcare, education, governance and social development, and media and entertainment. It also highlights three broader themes: positive computing, personalised human–computer interaction, and autonomy and self-determination. The review argues that three themes will be central to ongoing discussions and research by showing how they can be used to identify open questions related to the ethics of digital well-being.

Machine unlearning (MU) is often analyzed in terms of how it can facilitate the “right to be forgotten.” In this commentary, we show that MU can support the OECD’s five principles for trustworthy AI, which are influencing AI development and regulation worldwide. This makes it a promising tool to translate AI principles into practice. We also argue that the implementation of MU is not without ethical risks. To address these concerns and amplify the positive impact of MU, we offer policy recommendations across six categories to encourage the research and uptake of this potentially highly influential new technology.

This article reviews eight proposed strategies for solving the Symbol Grounding Problem (SGP), which was given its classic formulation in Harnad (1990). After a concise introduction, we provide an analysis of the requirement that must be satisfied by any hypothesis seeking to solve the SGP, the zero semantical commitment condition. We then use it to assess the eight strategies, which are organised into three main approaches: representationalism, semi-representationalism and non-representationalism. The conclusion is that all the strategies are semantically committed and hence that none of them provides a valid solution to the SGP, which remains an open problem.

This chapter follows the argument that personal medical data should be made available for scientific research by enabling and encouraging individuals to donate their medical records after death, provided that this can be done safely and ethically. While medical donation schemes with dedicated regulatory and ethical frameworks for blood, organ or tissue donations are already in place, no such ethical guidance currently exists with regard to personal medical data. In addressing this gap, this chapter presents the first ethical code for posthumous medical data donation (PMDD). It is based on five foundational principles and seeks to inform and guide the implementation of an effective and ethical PMDD scheme by addressing the key risks associated with the utilisation of personal health data for the promotion of the common good.