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  1. Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.
    Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...)
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  • An Analysis of the Impact of Brain-Computer Interfaces on Autonomy.Orsolya Friedrich, Eric Racine, Steffen Steinert, Johannes Pömsl & Ralf J. Jox - 2021 - Neuroethics 14 (1):17-29.
    Research conducted on Brain-Computer Interfaces has grown considerably during the last decades. With the help of BCIs, users can gain a wide range of functions. Our aim in this paper is to analyze the impact of BCIs on autonomy. To this end, we introduce three abilities that most accounts of autonomy take to be essential: the ability to use information and knowledge to produce reasons; the ability to ensure that intended actions are effectively realized ; and the ability to enact (...)
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  • A New Argument for No-Fault Compensation in Health Care: The Introduction of Artificial Intelligence Systems.Søren Holm, Catherine Stanton & Benjamin Bartlett - 2021 - Health Care Analysis 29 (3):171-188.
    Artificial intelligence systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5–10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation for medical error. It argues that the introduction of AI systems will provide an additional argument pointing towards no-fault compensation as the better legal solution to compensation for medical error in modern health care systems. The paper falls into four parts. The first part rehearses the main arguments for (...)
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  • Big Data in the Workplace: Privacy Due Diligence as a Human Rights-Based Approach to Employee Privacy Protection.Jeremias Adams-Prassl, Isabelle Wildhaber & Isabel Ebert - 2021 - Big Data and Society 8 (1).
    Data-driven technologies have come to pervade almost every aspect of business life, extending to employee monitoring and algorithmic management. How can employee privacy be protected in the age of datafication? This article surveys the potential and shortcomings of a number of legal and technical solutions to show the advantages of human rights-based approaches in addressing corporate responsibility to respect privacy and strengthen human agency. Based on this notion, we develop a process-oriented model of Privacy Due Diligence to complement existing frameworks (...)
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  • Assessing Biases, Relaxing Moralism: On Ground-Truthing Practices in Machine Learning Design and Application.Florian Jaton - 2021 - Big Data and Society 8 (1).
    This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here (...)
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  • Making Sense of Algorithms: Relational Perception of Contact Tracing and Risk Assessment During COVID-19.Ross Graham & Chuncheng Liu - 2021 - Big Data and Society 8 (1).
    Governments and citizens of nearly every nation have been compelled to respond to COVID-19. Many measures have been adopted, including contact tracing and risk assessment algorithms, whereby citizen whereabouts are monitored to trace contact with other infectious individuals in order to generate a risk status via algorithmic evaluation. Based on 38 in-depth interviews, we investigate how people make sense of Health Code, the Chinese contact tracing and risk assessment algorithmic sociotechnical assemblage. We probe how people accept or resist Health Code (...)
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  • The Algorithm Audit: Scoring the Algorithms That Score Us.Jovana Davidovic, Shea Brown & Ali Hasan - 2021 - Big Data and Society 8 (1).
    In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...)
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  • Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective.Erik Hermann - forthcoming - Journal of Business Ethics:1-19.
    Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To (...)
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  • Four Responsibility Gaps with Artificial Intelligence: Why They Matter and How to Address Them.Filippo Santoni de Sio & Giulio Mecacci - forthcoming - Philosophy and Technology:1-28.
    The notion of “responsibility gap” with artificial intelligence was originally introduced in the philosophical debate to indicate the concern that “learning automata” may make more difficult or impossible to attribute moral culpability to persons for untoward events. Building on literature in moral and legal philosophy, and ethics of technology, the paper proposes a broader and more comprehensive analysis of the responsibility gap. The responsibility gap, it is argued, is not one problem but a set of at least four interconnected problems (...)
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  • Problems with “Friendly AI”.Oliver Li - forthcoming - Ethics and Information Technology.
    On virtue ethical grounds, Barbro Fröding and Martin Peterson recently recommended that near-future AIs should be developed as ‘Friendly AI’. AI in social interaction with humans should be programmed such that they mimic aspects of human friendship. While it is a reasonable goal to implement AI systems interacting with humans as Friendly AI, I identify four issues that need to be addressed concerning Friendly AI with Fröding’s and Peterson’s understanding of Friendly AI as a starting point. In a first step, (...)
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  • Mental Time-Travel, Semantic Flexibility, and A.I. Ethics.Marcus Arvan - forthcoming - AI and Society:1-20.
    This article argues that existing approaches to programming ethical AI fail to resolve a serious moral-semantic trilemma, generating interpretations of ethical requirements that are either too semantically strict, too semantically flexible, or overly unpredictable. This paper then illustrates the trilemma utilizing a recently proposed ‘general ethical dilemma analyzer,’ GenEth. Finally, it uses empirical evidence to argue that human beings resolve the semantic trilemma using general cognitive and motivational processes involving ‘mental time-travel,’ whereby we simulate different possible pasts and futures. I (...)
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  • Political machines: a framework for studying politics in social machines.Orestis Papakyriakopoulos - forthcoming - AI and Society:1-18.
    In the age of ubiquitous computing and artificially intelligent applications, social machines serves as a powerful framework for understanding and interpreting interactions in socio-algorithmic ecosystems. Although researchers have largely used it to analyze the interactions of individuals and algorithms, limited attempts have been made to investigate the politics in social machines. In this study, I claim that social machines are per se political machines, and introduce a five-point framework for classifying influence processes in socio-algorithmic ecosystems. By drawing from scholars from (...)
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  • Toward an Ethics of AI Assistants: An Initial Framework.John Danaher - 2018 - Philosophy and Technology 31 (4):629-653.
    Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: searching, planning, messaging, scheduling and so on. Usage of such devices is effectively a form of algorithmic outsourcing: getting a smart algorithm to do something on your behalf. Many have expressed concerns about this algorithmic outsourcing. They claim that it is dehumanising, leads to cognitive degeneration, and robs us of our freedom and autonomy. (...)
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  • The Digital Phenotype: A Philosophical and Ethical Exploration.Michele Loi - 2019 - Philosophy and Technology 32 (1):155-171.
    The concept of the digital phenotype has been used to refer to digital data prognostic or diagnostic of disease conditions. Medical conditions may be inferred from the time pattern in an insomniac’s tweets, the Facebook posts of a depressed individual, or the web searches of a hypochondriac. This paper conceptualizes digital data as an extended phenotype of humans, that is as digital information produced by humans and affecting human behavior and culture. It argues that there are ethical obligations to persons (...)
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  • What has the Trolley Dilemma Ever Done for Us ? On Some Recent Debates About the Ethics of Self-Driving Cars.Andreas Wolkenstein - 2018 - Ethics and Information Technology 20 (3):163-173.
    Self-driving cars currently face a lot of technological problems that need to be solved before the cars can be widely used. However, they also face ethical problems, among which the question of crash-optimization algorithms is most prominently discussed. Reviewing current debates about whether we should use the ethics of the Trolley Dilemma as a guide towards designing self-driving cars will provide us with insights about what exactly ethical research does. It will result in the view that although we need the (...)
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  • Algorithmic Augmentation of Democracy: Considering Whether Technology Can Enhance the Concepts of Democracy and the Rule of Law Through Four Hypotheticals.Paul Burgess - forthcoming - AI and Society:1-16.
    The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion :114–126, 2015). In this paper, I confront this intuition and suggest that a more ‘extreme’ form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental concepts of democracy and the Rule of (...)
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  • The Influence of Business Incentives and Attitudes on Ethics Discourse in the Information Technology Industry.Sanju Ahuja & Jyoti Kumar - forthcoming - Philosophy and Technology:1-26.
    As information technologies have become synonymous with progress in modern society, several ethical concerns have surfaced about their societal implications. In the past few decades, information technologies have had a value-laden impact on social evolution. However, there is limited agreement on the responsibility of businesses and innovators concerning the ethical aspects of information technologies. There is a need to understand the role of business incentives and attitudes in driving technological progress and to understand how they steer the ethics discourse on (...)
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  • The Ethics of Algorithms: Key Problems and Solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2021 - AI and Society.
    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 (...)
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  • Making the black box society transparent.Daniel Innerarity - forthcoming - AI and Society:1-7.
    The growing presence of smart devices in our lives turns all of society into something largely unknown to us. The strategy of demanding transparency stems from the desire to reduce the ignorance to which this automated society seems to condemn us. An evaluation of this strategy first requires that we distinguish the different types of non-transparency. Once we reveal the limits of the transparency needed to confront these devices, the article examines the alternative strategy of explainable artificial intelligence and concludes (...)
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  • Towards Transparency by Design for Artificial Intelligence.Heike Felzmann, Eduard Fosch-Villaronga, Christoph Lutz & Aurelia Tamò-Larrieux - 2020 - Science and Engineering Ethics 26 (6):3333-3361.
    In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making environments. With the rise of artificial intelligence and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different (...)
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  • Getting into the engine room: a blueprint to investigate the shadowy steps of AI ethics.Johan Rochel & Florian Evéquoz - forthcoming - AI and Society:1-14.
    Enacting an AI system typically requires three iterative phases where AI engineers are in command: selection and preparation of the data, selection and configuration of algorithmic tools, and fine-tuning of the different parameters on the basis of intermediate results. Our main hypothesis is that these phases involve practices with ethical questions. This paper maps these ethical questions and proposes a way to address them in light of a neo-republican understanding of freedom, defined as absence of domination. We thereby identify different (...)
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  • Just Data? Solidarity and Justice in Data-Driven Medicine.Matthias Braun & Patrik Hummel - 2020 - Life Sciences, Society and Policy 16 (1):1-18.
    This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could—especially considering current developments in big data and artificial intelligence—compromise the right by leading to injustices and (...)
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  • Digital Hyperconnectivity and the Self.Rogers Brubaker - 2020 - Theory and Society 49 (5-6):771-801.
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  • Ethical and Legal Challenges of Informed Consent Applying Artificial Intelligence in Medical Diagnostic Consultations.Kristina Astromskė, Eimantas Peičius & Paulius Astromskis - forthcoming - AI and Society.
    This paper inquiries into the complex issue of informed consent applying artificial intelligence in medical diagnostic consultations. The aim is to expose the main ethical and legal concerns of the New Health phenomenon, powered by intelligent machines. To achieve this objective, the first part of the paper analyzes ethical aspects of the alleged right to explanation, privacy, and informed consent, applying artificial intelligence in medical diagnostic consultations. This analysis is followed by a legal analysis of the limits and requirements for (...)
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  • Understanding and Managing Responsible Innovation.Hans Bennink - 2020 - Philosophy of Management 19 (3):317-348.
    As a relational concept, responsible innovation can be made more tangible by asking innovation of what and responsibility of whom for what? Arranging the scattered field of responsible innovation comprehensively, starting from an anthropological point of view, into five fields of tension and five categories of spearheads, may be theoretically and practically helpful while offering suggestions for both research and management.
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  • Artificial Intelligence and Medical Humanities.Kirsten Ostherr - forthcoming - Journal of Medical Humanities.
    The use of artificial intelligence in healthcare has led to debates about the role of human clinicians in the increasingly technological contexts of medicine. Some researchers have argued that AI will augment the capacities of physicians and increase their availability to provide empathy and other uniquely human forms of care to their patients. The human vulnerabilities experienced in the healthcare context raise the stakes of new technologies such as AI, and the human dimensions of AI in healthcare have particular significance (...)
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  • A Taxonomy of Human–Machine Collaboration: Capturing Automation and Technical Autonomy.Monika Simmler & Ruth Frischknecht - 2021 - AI and Society 36 (1):239-250.
    Due to the ongoing advancements in technology, socio-technical collaboration has become increasingly prevalent. This poses challenges in terms of governance and accountability, as well as issues in various other fields. Therefore, it is crucial to familiarize decision-makers and researchers with the core of human–machine collaboration. This study introduces a taxonomy that enables identification of the very nature of human–machine interaction. A literature review has revealed that automation and technical autonomy are main parameters for describing and understanding such interaction. Both aspects (...)
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  • Listening to Distant Voices.Claudius Messner - 2020 - International Journal for the Semiotics of Law - Revue Internationale de Sémiotique Juridique 33 (4):1143-1173.
    The “digital revolution”, which the juridical observer is asked to tackle, as well as the consequences that legal experts have to deal with, is not an abstract phenomenon. Digitalisation is a consequence and the latest manifestation of the Western culture of the machine. This framework shapes the various concepts of language, political community, and justice, on which, in turn, the diverse current views of the interpreter depend. The twentieth century’s globalisation of legal civilisation has perfected the machinery of legal talk. (...)
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  • The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - forthcoming - Synthese:1-32.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal (...)
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  • Discrimination in the Age of Artificial Intelligence.Bert Heinrichs - forthcoming - AI and Society:1-12.
    In this paper, I examine whether the use of artificial intelligence and automated decision-making aggravates issues of discrimination as has been argued by several authors. For this purpose, I first take up the lively philosophical debate on discrimination and present my own definition of the concept. Equipped with this account, I subsequently review some of the recent literature on the use AI/ADM and discrimination. I explain how my account of discrimination helps to understand that the general claim in view of (...)
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  • The Right to Refuse Diagnostics and Treatment Planning by Artificial Intelligence.Thomas Ploug & Søren Holm - 2020 - Medicine, Health Care and Philosophy 23 (1):107-114.
    In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to the physician’s role in the patients’ formation of and acting on personal preferences and values, the bias and opacity problem of AI systems, and rational concerns about the future societal effects of introducing AI systems in the health care sector.
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  • Mobile Health Ethics and the Expanding Role of Autonomy.Bettina Schmietow & Georg Marckmann - 2019 - Medicine, Health Care and Philosophy 22 (4):623-630.
    Mhealth technology is mushrooming world-wide and, in a variety of forms, reaches increasing numbers of users in ever-widening contexts and virtually independent from standard medical evidence assessment. Yet, debate on the broader societal impact including in particular mapping and classification of ethical issues raised has been limited. This article, as part of an ongoing empirically informed ethical research project, provides an overview of ethical issues of mhealth applications with a specific focus on implications on autonomy as a key notion in (...)
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  • Perceptual Bias and Technical Metapictures: Critical Machine Vision as a Humanities Challenge.Fabian Offert & Peter Bell - forthcoming - AI and Society.
    In many critical investigations of machine vision, the focus lies almost exclusively on dataset bias and on fixing datasets by introducing more and more diverse sets of images. We propose that machine vision systems are inherently biased not only because they rely on biased datasets but also because theirperceptual topology, their specific way of representing the visual world, gives rise to a new class of bias that we callperceptual bias. Concretely, we define perceptual topology as the set of those inductive (...)
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  • Algorithmic Paranoia: The Temporal Governmentality of Predictive Policing.Bonnie Sheehey - 2019 - Ethics and Information Technology 21 (1):49-58.
    In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...)
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  • Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2019 - Philosophy and Technology 32 (4):661-683.
    We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...)
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  • Algo-Rhythms and the Beat of the Legal Drum.Ugo Pagallo - 2018 - Philosophy and Technology 31 (4):507-524.
    The paper focuses on concerns and legal challenges brought on by the use of algorithms. A particular class of algorithms that augment or replace analysis and decision-making by humans, i.e. data analytics and machine learning, is under scrutiny. Taking into account Balkin’s work on “the laws of an algorithmic society”, attention is drawn to obligations of transparency, matters of due process, and accountability. This US-centric analysis on drawbacks and loopholes of current legal systems is complemented with the analysis of norms (...)
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  • A Constructionist Philosophy of Logic.Patrick Allo - 2017 - Minds and Machines 27 (3):545-564.
    This paper develops and refines the suggestion that logical systems are conceptual artefacts that are the outcome of a design-process by exploring how a constructionist epistemology and meta-philosophy can be integrated within the philosophy of logic.
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  • Evil and roboethics in management studies.Enrico Beltramini - 2019 - AI and Society 34 (4):921-929.
    In this article, I address the issue of evil and roboethics in the context of management studies and suggest that management scholars should locate evil in the realm of the human rather than of the artificial. After discussing the possibility of addressing the reality of evil machines in ontological terms, I explore users’ reaction to robots in a social context. I conclude that the issue of evil machines in management is more precisely a case of technology anthropomorphization.
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  • Research and Practice of AI Ethics: A Case Study Approach Juxtaposing Academic Discourse with Organisational Reality.Bernd Stahl, Kevin Macnish, Tilimbe Jiya, Laurence Brooks, Josephina Antoniou & Mark Ryan - 2021 - Science and Engineering Ethics 27 (2):1-29.
    This study investigates the ethical use of Big Data and Artificial Intelligence technologies —using an empirical approach. The paper categorises the current literature and presents a multi-case study of 'on-the-ground' ethical issues that uses qualitative tools to analyse findings from ten targeted case-studies from a range of domains. The analysis coalesces identified singular ethical issues,, into clusters to offer a comparison with the proposed classification in the literature. The results show that despite the variety of different social domains, fields, and (...)
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  • Human-Aligned Artificial Intelligence is a Multiobjective Problem.Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin & Jane Mummery - 2018 - Ethics and Information Technology 20 (1):27-40.
    As the capabilities of artificial intelligence systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility paradigm provides insufficient (...)
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  • Comparative Legal Study on Privacy and Personal Data Protection for Robots Equipped with Artificial Intelligence: Looking at Functional and Technological Aspects.Kaori Ishii - 2019 - AI and Society 34 (3):509-533.
    This paper undertakes a comparative legal study to analyze the challenges of privacy and personal data protection posed by Artificial Intelligence embedded in Robots, and to offer policy suggestions. After identifying the benefits from various AI usages and the risks posed by AI-related technologies, I then analyze legal frameworks and relevant discussions in the EU, USA, Canada, and Japan, and further consider the efforts of Privacy by Design originating in Ontario, Canada. While various AI usages provide great convenience, many issues, (...)
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  • From Individual to Group Privacy in Big Data Analytics.Brent Mittelstadt - 2017 - Philosophy and Technology 30 (4):475-494.
    Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that does not necessarily align (...)
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  • Transparency You Can Trust: Transparency Requirements for Artificial Intelligence Between Legal Norms and Contextual Concerns.Aurelia Tamò-Larrieux, Christoph Lutz, Eduard Fosch Villaronga & Heike Felzmann - 2019 - Big Data and Society 6 (1).
    Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect (...)
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  • Ethical Guidelines for the Use of Artificial Intelligence and the Challenges From Value Conflicts.Thomas Søbirk Petersen - 2021 - Etikk I Praksis - Nordic Journal of Applied Ethics 1:25-40.
    The aim of this article is to articulate and critically discuss different answers to the following question: How should decision-makers deal with conflicts that arise when the values usually entailed in ethical guidelines – such as accuracy, privacy, non-discrimination and transparency – for the use of Artificial Intelligence clash with one another? To begin with, I focus on clarifying some of the general advantages of using such guidelines in an ethical analysis of the use of AI. Some disadvantages will also (...)
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  • Artificial Intelligence and the ‘Good Society’: The US, EU, and UK Approach.Corinne Cath, Sandra Wachter, Brent Mittelstadt, Mariarosaria Taddeo & Luciano Floridi - 2018 - Science and Engineering Ethics 24 (2):505-528.
    In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of a ‘good (...)
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  • Varieties of Artifacts: Embodied, Perceptual, Cognitive, and Affective.Richard Heersmink - 2021 - Topics in Cognitive Science:1-24.
    The primary goal of this essay is to provide a comprehensive overview and analysis of the various relations between material artifacts and the embodied mind. A secondary goal of this essay is to identify some of the trends in the design and use of artifacts. First, based on their functional properties, I identify four categories of artifacts co-opted by the embodied mind, namely (1) embodied artifacts, (2) perceptual artifacts, (3) cognitive artifacts, and (4) affective artifacts. These categories can overlap and (...)
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  • The Ethical Gravity Thesis: Marrian Levels and the Persistence of Bias in Automated Decision-Making Systems.Atoosa Kasirzadeh & Colin Klein - 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (AIES '21).
    Computers are used to make decisions in an increasing number of domains. There is widespread agreement that some of these uses are ethically problematic. Far less clear is where ethical problems arise, and what might be done about them. This paper expands and defends the Ethical Gravity Thesis: ethical problems that arise at higher levels of analysis of an automated decision-making system are inherited by lower levels of analysis. Particular instantiations of systems can add new problems, but not ameliorate more (...)
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  • The Ethics of AI in Health Care: A Mapping Review.Jessica Morley, Caio C. V. Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo & Luciano Floridi - 2020 - Social Science & Medicine 260.
    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 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 (...)
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  • From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles Into Practices.Jessica Morley, Luciano Floridi, Libby Kinsey & Anat Elhalal - 2020 - Science and Engineering Ethics 26 (4):2141-2168.
    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 (...)
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  • The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.David S. Watson - 2019 - Minds and Machines 29 (3):417-440.
    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 (...)
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