Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...) and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

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, 2016). The goals 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. (shrink)

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, 2016). The goals 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. (shrink)

This paper presents an emerging aspect of intentionality through recent Artificial Intelligence (AI) developments in art and design. Our main thesis is that, if we focus just on the outcome of the artistic process, the intentionality of the artist does not have any relevance. Intention is measured as a result of actions regardless of whether they are human-based or not as long as there is an esthetical value intersubjectively acknowledged. In other words, what matters is the ‘intentio’ embedded in the (...) work of art (the output) rather than the ‘intendere’ (the process). By considering the former aspect, intentionality becomes free of human intervention, therefore 'unintentional'. This thesis is supported through a range of examples related to AI in general and AI art in particular. However, in the case of a self-driving vehicle, the macro-goal of the system is fixed by humans, even though the AI system itself will take care of decomposing the macro-goal into a set of micro-goals and take the corresponding decisions. In the case of art, the system does not need to have any exterior goal to the extent that art is pure freedom of expression, as many philosophers have acknowledged. In this sense, the decisions of any AI system generating art or design are independent of humans and must be judged only on the ground of the final outcome. A work of art will be judged ‘artistic’ to the extent that humans will recognize an artistic intent in the work itself. In particular, the introduction of random processes makes an AI-generated artwork fully independent of human creativity. (shrink)

The use of artificial intelligence (AI) to make high-stakes decisions is sometimes thought to create a troubling responsibility gap – that is, a situation where nobody can be held morally responsible for the outcomes that are brought about. However, philosophers and practitioners have recently claimed that, even though no individual can be held morally responsible, groups of individuals might be. Consequently, they think, we have less to fear from the use of AI than might appear to be the case. This (...) paper assesses this claim. Drawing on existing philosophical models of collective responsibility, I consider whether changing focus from the individual to the collective level can help us identify a locus of responsibility in a greater range of cases of AI deployment. I find that appeal to collective responsibility will be of limited use in filling the responsibility gap: the models considered either do not apply to the case at hand or else the relevant sort of collective responsibility, even if present, will not be sufficient to remove the costs that are often associated with an absence of responsibility. (shrink)

This paper argues that there is an urgent need for an international doctrine for cyberspace skirmishes before they escalate into conventional warfare.

Defence agencies across the globe identify artificial intelligence as a key technology to maintain an edge over adversaries. As a result, efforts to develop or acquire AI capabilities for defence are growing on a global scale. Unfortunately, they remain unmatched by efforts to define ethical frameworks to guide the use of AI in the defence domain. This article provides one such framework. It identifies five principles—justified and overridable uses, just and transparent systems and processes, human moral responsibility, meaningful human control (...) and reliable AI systems—and related recommendations to foster ethically sound uses of AI for national defence purposes. (shrink)

In this article, we focus on the attribution of moral responsibility for the actions of autonomous weapons systems (AWS). To do so, we suggest that the responsibility gap can be closed if human agents can take meaningful moral responsibility for the actions of AWS. This is a moral responsibility attributed to individuals in a justified and fair way and which is accepted by individuals as an assessment of their own moral character. We argue that, given the unpredictability of AWS, meaningful (...) moral responsibly can only be discharged by human agents who are willing to take a moral gambit: they decide to design/develop/deploy AWS despite the uncertainty about the effects an AWS may produce, hoping that unintended and unwanted or unforeseen outcomes may never occurs, but also accepting to be held responsible if such outcomes will occur. We argue that, while a moral gambit is permissible for the use of non-lethal AWS, this is not the case for the actions of lethal autonomous weapon systems. (shrink)

This paper intends to contribute to the emerging literature on the ethical problems posed by quantum computing and quantum technologies in general. The key ethical questions are as follows: Does quantum computing pose new ethical problems, or are those raised by quantum computing just a different version of the same ethical problems raised by other technologies, such as nanotechnologies, nuclear plants, or cloud computing? In other words, what is new in quantum computing from an ethical point of view? The paper (...) aims to answer these two questions by (a) developing an analysis of the existing literature on the ethical and social aspects of quantum computing and (b) identifying and analyzing the main ethical problems posed by quantum computing. The conclusion is that quantum computing poses completely new ethical issues that require new conceptual tools and methods. (shrink)

: Artificial intelligences and robots increasingly mimic human mental powers and intelligent behaviour. However, many authors claim that ascribing human mental powers to them is both conceptually mistaken and morally dangerous. This article defends the view that artificial intelligences can have human-like mental powers, by claiming that both human and artificial minds can be seen as extended minds – along the lines of Chalmers and Clark’s view of mind and cognition. The main idea of this article is that the Extended (...) Mind Model is independently plausible and can easily be extended to artificial intelligences, providing a solid base for concluding that artificial intelligences possess minds. This may warrant viewing them as morally responsible agents. Keywords: Artificial Intelligence; Mind; Moral Responsibility; Extended Cognition Intelligenze artificiali come menti estese. Perché no? Riassunto: Intelligenze artificiali e robot simulano in misura sempre crescente le capacità mentali e i comportamenti intelligenti umani. Molti autori, tuttavia, sostengono che attribuire loro capacità mentali umane sia concettualmente errato e moralmente pericoloso. In questo lavoro si difende l’idea per cui le intelligenze artificiali possano avere capacità mentali simili a quelle umane, sostenendo che menti umane e artificiali possano essere considerate come menti estese – sulla scorta della prospettiva di Chalmers e Clark circa la mente e la cognizione. L’idea principale alla base di questo lavoro è che il Modello della Mente Estesa abbia plausibilità a prescindere e che possa essere facilmente esteso alle intelligenze artificiali, fornendo una base solida per concludere che le intelligenze artificiali possiedano delle menti e si possano considerare come agenti moralmente responsabili. Parole chiave: Intelligenza artificiale; Mente; Responsabilità morale; Conoscenza estesa. (shrink)

The paper examines the open problems that experts of space law shall increasingly address over the next few years, according to four different sets of legal issues. Such differentiation sheds light on what is old and what is new with today’s troubles of space law, e.g., the privatization of space, vis-à-vis the challenges that AI raises in this field. Some AI challenges depend on its unique features, e.g., autonomy and opacity, and how they affect pillars of the law, whether on (...) Earth or in space missions. The paper insists on a further class of legal issues that AI systems raise, however, only in outer space. We shall never overlook the constraints of a hazardous and hostile environment, such as on a mission between Mars and the Moon. The aim of this paper is to illustrate what is still mostly unexplored or in its infancy in this kind of research, namely, the fourfold ways in which the uniqueness of AI and that of outer space impact both ethical and legal standards. Such standards shall provide for thresholds of evaluation according to which courts and legislators evaluate the pros and cons of technology. Our claim is that a new generation of sui generis standards of space law, stricter or more flexible standards for AI systems in outer space, down to the “principle of equality” between human standards and robotic standards, will follow as a result of this twofold uniqueness of AI and of outer space. (shrink)

While the consequences of mathematically-based software, algorithms and strategies have become ever wider and better appreciated, ethical reflection on mathematics has remained primitive. We review the somewhat disconnected suggestions of commentators in recent decades with a view to piecing together a coherent approach to ethics in mathematics. Calls for a Hippocratic Oath for mathematicians are examined and it is concluded that while lessons can be learned from the medical profession, the relation of mathematicians to those affected by their work is (...) significantly different. There is something to be learned also from the codes of conduct of cognate but professionalised quantitative disciplines such as engineering and accountancy, as well as from legal principles bearing on professional work. We conclude with recommendations that professional societies in mathematics should sponsor an (international) code of ethics, institutional mission statements for mathematicians and syllabuses of ethics courses for incorporation into mathematics degrees. (shrink)

This article highlights the limitations of the tendency to frame health- and wellbeing-related digital tools (mHealth technologies) as empowering devices, especially as they play an increasingly important role in the National Health Service (NHS) in the UK. It argues that mHealth technologies should instead be framed as digital companions. This shift from empowerment to companionship is advocated by showing the conceptual, ethical, and methodological issues challenging the narrative of empowerment, and by arguing that such challenges, as well as the risk (...) of medical paternalism, can be overcome by focusing on the potential for mHealth tools to mediate the relationship between recipients of clinical advice and givers of clinical advice, in ways that allow for contextual flexibility in the balance between patiency and agency. The article concludes by stressing that reframing the narrative cannot be the only means for avoiding harm caused to the NHS as a healthcare system by the introduction of mHealth tools. Future discussion will be needed on the overarching role of responsible design. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

As the range of potential uses for Artificial Intelligence, in particular machine learning, has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of (...) AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible or too strict. This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of (...) automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

Machine learning-based AI algorithms lack transparency. In this article, I offer an interpretation of AI’s explainability problem and highlight its ethical saliency. I try to make the case for the legal enforcement of a strong explainability requirement: human organizations which decide to automate decision-making should be legally obliged to demonstrate the capacity to explain and justify the algorithmic decisions that have an impact on the wellbeing, rights, and opportunities of those affected by the decisions. This legal duty can be derived (...) from the demands of Rawlsian public reason. In the second part of the paper, I try to show that the argument from the limitations of human cognition fails to get AI off the hook of public reason. Against a growing trend in AI ethics, my main argument is that the analogy between human minds and artificial neural networks fails because it suffers from an atomistic bias which makes it blind to the social and institutional dimension of human reasoning processes. I suggest that developing interpretive AI algorithms is not the only possible answer to the explainability problem; social and institutional answers are also available and in many cases more trustworthy than techno-scientific ones. (shrink)

Artificial intelligence 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 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. (shrink)

Wearable robots and exoskeletons are relatively new technologies designed for assisting and augmenting human motor functions. Due to their different possible design applications and their intimate connection to the human body, they come with specific ethical, legal, and social issues, which have not been much explored in the recent ELS literature. This paper draws on expert consultations and a literature review to provide a taxonomy of the most important ethical, legal, and social issues of wearable robots. These issues are categorized (...) in wearable robots and the self, wearable robots and the other, and wearable robots in society. (shrink)

Should you be targeted by police for a crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics (...) guidelines (IEEE, EU, and RIKEN, etc.), we refine three basic moral principles specific to person-based PP. We also derive further requirements from case studies, including debates in Chicago, New Orleans, San Francisco, Tokyo, and cities in China. Instead of rejecting PP programs, we analyse what necessary conditions should be met for using the tool to achieve social good. While acknowledging its risks, we conclude that the person-based PP could be beneficial in community policing, especially when merging into a larger governance framework of the social safety net. (shrink)

Current advances in research, development and application of artificial intelligence systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the “disruptive” potentials of new AI technologies. Designed as a semi-systematic evaluation, this paper analyzes and compares 22 guidelines, highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, (...) I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems—and how the effectiveness in the demands of AI ethics can be improved. (shrink)

Modern digital technologies—from web-based services to Artificial Intelligence (AI) solutions—increasingly affect the daily lives of billions of people. Such innovation brings huge opportunities, but also concerns about design, development, and deployment of digital technologies. This article identifies and discusses five clusters of risk in the international debate about digital ethics: ethics shopping; ethics bluewashing; ethics lobbying; ethics dumping; and ethics shirking.

In the highly competitive global market, characterized by rapid political, economic, environmental and technological changes, there has been an increased interest in the role of ethics for shaping corporate actions and highlighting the essential tasks and measures to fulfill two generic missions: support enterprises to make distinctive, lasting and substantial improvements in their performance and build a great firm that attracts, develops, excites and retains exceptional people. This paper addresses the issues arising from opposing forces, namely on the one hand (...) the extremely challenging external environment and on the other hand the increasing demand for responsible business behaviour and ethical decision-making focused on the shared and sustained value creation to serve the interests of all stakeholders, society and the environment. Primary objective is to provide coherent and compact definitions for the key values dealing with professionalism, equality and sustainability that should be clearly defined, thus providing the key drivers for creating a working environment that inspires and motivates employees on one hand; and promote each enterprise socioeconomic footprint in the regional and global business ecosystem. When handling the concepts of responsibility, ethical codes, integrity or trust, philosophy provides an invaluable framework and a foundational basis on which to create and maintain sustainable structures and processes for the business of the new era. (shrink)

Purpose This study advances a reconceptualization of data and information which overcomes normative understandings often contained in data policies at national and international levels. This study aims to propose a conceptual framework that moves beyond subject- and collective-centric normative understandings. Design/methodology/approach To do so, this study discusses the European Union (EU) and China’s approaches to data-driven technologies highlighting their similarities and differences when it comes to the vision underpinning how tech innovation is shaped. Findings Regardless of the different attention to (...) the subject (the EU) and the collective (China), the normative understandings of technology by both actors remain trapped into a positivist approach that overlooks all that is not and cannot be turned into data, thus hindering the elaboration of a more holistic ecological thinking merging humans and technologies. Originality/value Revising the philosophical and political debate on data and data-driven technologies, a third way is elaborated, i.e. federated data as commons. This third way puts the subject as part by default of a collective at the centre of discussion. This framing can serve as the basis for elaborating sociotechnical alternatives when it comes to define and regulate the mash-up of humans and technology. (shrink)

The relationship between a human being and an AI system has to be considered as a collaborative process between two agents during the performance of an activity. When there is a collaboration between two people, a fundamental characteristic of that collaboration is that there is co-supervision, with each agent supervising the actions of the other. Such supervision ensures that the activity achieves its objectives, but it also means that responsibility for the consequences of the activity is shared. If there is (...) no co-supervision, neither collaborator can be held co-responsible for the actions of the other. When the collaboration is between a person and an AI system, co-supervision is also necessary to ensure that the objectives of the activity are achieved, but this also means that there is co-responsibility for the consequences of the activities. Therefore, if each agent's responsibility for the consequences of the activity depends on the effectiveness and efficiency of the supervision that that agent performs over the other agent's actions, it will be necessary to take into account the way in which that supervision is carried out and the factors on which it depends. In the case of the human supervision of the actions of an AI system, there is a wealth of psychological research that can help us to establish cognitive and non-cognitive boundaries and their relationship to the responsibility of humans collaborating with AI systems. There is also psychological research on how an external observer supervises and evaluates human actions. This research can be used to programme AI systems in such a way that the boundaries of responsibility for AI systems can be established. In this article, we will describe some examples of how such research on the task of supervising the actions of another agent can be used to establish lines of shared responsibility between a human being and an AI system. The article will conclude by proposing that we should develop a methodology for assessing responsibility based on the results of the collaboration between a human being and an AI agent during the performance of one common activity. (shrink)

The proposition that digital innovations can put people in charge of their health has been accompanied by prolific talk of empowerment. In this paper we consider ethical challenges and opportunities of trying to achieve justice and empowerment using digital health initiatives. The language of empowerment can misleadingly suggest that by using technology, people can control their health and take responsibility for health outcomes to a greater degree than is realistic or fair. Also, digital health empowerment often primarily reaches people who (...) already have high technological and health literacy, leaving others behind. We critically investigate whether the concept of health empowerment could be re-engineered to mean something different, namely the process of improving the health literacy and access of those who least possess it, in line with recent proposals for conceptual engineering in the service of justice. We settle on the weaker conclusion that underlying ethical values including justice should be used to interpret the existing concept of empowerment. To conclude, we take a high-level view of various strategies for achieving the ethical value associated with digital health empowerment. (shrink)

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key (...) ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare. (shrink)

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the (...) theory of AI ethics principles and the practical design of AI systems. In previous work , we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service’. (shrink)

It has been suggested that to overcome the challenges facing the UK’s National Health Service (NHS) of an ageing population and reduced available funding, the NHS should be transformed into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions between human, artificial, and hybrid (semi-artificial) agents. This transformation process would offer significant benefit to patients, clinicians, and the overall system, but it would also rely on a fundamental transformation of the healthcare system in a way that (...) poses significant governance challenges. In this article, we argue that a fruitful way to overcome these challenges is by adopting a pro-ethical approach to design that analyses the system as a whole, keeps society-in-the-loop throughout the process, and distributes responsibility evenly across all nodes in the system. (shrink)