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  1. “Please understand we cannot provide further information”: evaluating content and transparency of GDPR-mandated AI disclosures.Alexander J. Wulf & Ognyan Seizov - 2024 - AI and Society 39 (1):235-256.
    The General Data Protection Regulation (GDPR) of the EU confirms the protection of personal data as a fundamental human right and affords data subjects more control over the way their personal information is processed, shared, and analyzed. However, where data are processed by artificial intelligence (AI) algorithms, asserting control and providing adequate explanations is a challenge. Due to massive increases in computing power and big data processing, modern AI algorithms are too complex and opaque to be understood by most data (...)
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  • Body stakes: an existential ethics of care in living with biometrics and AI.Amanda Lagerkvist, Matilda Tudor, Jacek Smolicki, Charles M. Ess, Jenny Eriksson Lundström & Maria Rogg - 2024 - AI and Society 39 (1):169-181.
    This article discusses the key existential stakes of implementing biometrics in human lifeworlds. In this pursuit, we offer a problematization and reinvention of central values often taken for granted within the “ethical turn” of AI development and discourse, such as autonomy, agency, privacy and integrity, as we revisit basic questions about what it means to be human and embodied. Within a framework of existential media studies, we introduce an existential ethics of care—through a conversation between existentialism, virtue ethics, a feminist (...)
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  • What about investors? ESG analyses as tools for ethics-based AI auditing.Matti Minkkinen, Anniina Niukkanen & Matti Mäntymäki - 2024 - AI and Society 39 (1):329-343.
    Artificial intelligence (AI) governance and auditing promise to bridge the gap between AI ethics principles and the responsible use of AI systems, but they require assessment mechanisms and metrics. Effective AI governance is not only about legal compliance; organizations can strive to go beyond legal requirements by proactively considering the risks inherent in their AI systems. In the past decade, investors have become increasingly active in advancing corporate social responsibility and sustainability practices. Including nonfinancial information related to environmental, social, and (...)
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  • The achievement gap thesis reconsidered: artificial intelligence, automation, and meaningful work.Lucas Scripter - forthcoming - AI and Society:1-14.
    John Danaher and Sven Nyholm have argued that automation, especially of the sort powered by artificial intelligence, poses a threat to meaningful work by diminishing the chances for meaning-conferring workplace achievement, what they call “achievement gaps”. In this paper, I argue that Danaher and Nyholm’s achievement gap thesis suffers from an ambiguity. The weak version of the thesis holds that automation may result in the appearance of achievement gaps, whereas the strong version holds that automation may result on balance loss (...)
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  • Sobre a eficiência da ética como ferramenta de governança da inteligência artificial.Nicholas Kluge Corrêa, Nythamar Fernandes de Oliveira & Diogo Fernando Massmann - 2022 - Veritas – Revista de Filosofia da Pucrs 67 (1):e42584.
    A 4ª Revolução Industrial é o culminar da era digital. Atualmente, tecnologias como robótica, nanotecnologia, genética e inteligência artificial prometem transformar nosso mundo e a maneira como vivemos. O campo da Segurança e da Ética da Inteligência Artificial são áreas de pesquisa emergentes que vêm ganhando popularidade nos últimos anos. Diversas organizações de cunho privado, público e não governamentais têm publicado diretrizes propondo princípios éticos para a regulamentação do uso e desenvolvimento de sistemas inteligentes autônomos. Meta-análises do campo de pesquisa (...)
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  • Why bad coffee? Explaining BDI agent behaviour with valuings.Michael Winikoff, Galina Sidorenko, Virginia Dignum & Frank Dignum - 2021 - Artificial Intelligence 300 (C):103554.
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  • The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):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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  • The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2021 - Synthese 198 (10):9211-9242.
    We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealisedexplanation gamein which players collaborate to find the best explanation(s) 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 patterns of (...)
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  • Artificial intelligence and the value of transparency.Joel Walmsley - 2021 - AI and Society 36 (2):585-595.
    Some recent developments in Artificial Intelligence—especially the use of machine learning systems, trained on big data sets and deployed in socially significant and ethically weighty contexts—have led to a number of calls for “transparency”. This paper explores the epistemological and ethical dimensions of that concept, as well as surveying and taxonomising the variety of ways in which it has been invoked in recent discussions. Whilst “outward” forms of transparency may be straightforwardly achieved, what I call “functional” transparency about the inner (...)
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  • Socio-ethical implications of using AI in accelerating SDG3 in Least Developed Countries.Kutoma Wakunuma, Tilimbe Jiya & Suleiman Aliyu - 2020 - Journal of Responsible Technology 4:100006.
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  • Is tomorrow’s car appealing today? Ethical issues and user attitudes beyond automation.Darja Vrščaj, Sven Nyholm & Geert P. J. Verbong - 2020 - AI and Society 35 (4):1033-1046.
    The literature on ethics and user attitudes towards AVs discusses user concerns in relation to automation; however, we show that there are additional relevant issues at stake. To assess adolescents’ attitudes regarding the ‘car of the future’ as presented by car manufacturers, we conducted two studies with over 400 participants altogether. We used a mixed methods approach in which we combined qualitative and quantitative methods. In the first study, our respondents appeared to be more concerned about other aspects of AVs (...)
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  • Weak Signal-Oriented Investigation of Ethical Dissonance Applied to Unsuccessful Mobility Experiences Linked to Human–Machine Interactions.F. Vanderhaegen - 2021 - Science and Engineering Ethics 27 (1):1-25.
    Ethical dissonance arises from conflicts between beliefs or behaviors and affects ethical factors such as normality or conformity. This paper proposes a weak signal-oriented framework to investigate ethical dissonance from experiences linked to human–machine interactions. It is based on a systems engineering principle called human-systems inclusion, which considers any experience feedback of weak signals as beneficial to learn. The framework studies weak signal-based scenarios from testimonies of individual experiences and these scenarios are assessed by other people. For this purpose, the (...)
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  • Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?Frank Ursin, Felix Lindner, Timo Ropinski, Sabine Salloch & Cristian Timmermann - 2023 - Ethik in der Medizin 35 (2):173-199.
    Definition of the problem The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which (...)
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  • The Role of Engineers in Harmonising Human Values for AI Systems Design.Steven Umbrello - 2022 - Journal of Responsible Technology 10 (July):100031.
    Most engineers Fwork within social structures governing and governed by a set of values that primarily emphasise economic concerns. The majority of innovations derive from these loci. Given the effects of these innovations on various communities, it is imperative that the values they embody are aligned with those societies. Like other transformative technologies, artificial intelligence systems can be designed by a single organisation but be diffused globally, demonstrating impacts over time. This paper argues that in order to design for this (...)
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  • Value Sensitive Design to Achieve the UN SDGs with AI: A Case of Elderly Care Robots.Steven Umbrello, Marianna Capasso, Maurizio Balistreri, Alberto Pirni & Federica Merenda - 2021 - Minds and Machines 31 (3):395-419.
    Healthcare is becoming increasingly automated with the development and deployment of care robots. There are many benefits to care robots but they also pose many challenging ethical issues. This paper takes care robots for the elderly as the subject of analysis, building on previous literature in the domain of the ethics and design of care robots. Using the value sensitive design approach to technology design, this paper extends its application to care robots by integrating the values of care, values that (...)
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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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  • The ethics of algorithms: key problems and solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2022 - AI and Society 37 (1):215-230.
    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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  • From Pluralistic Normative Principles to Autonomous-Agent Rules.Beverley Townsend, Colin Paterson, T. T. Arvind, Gabriel Nemirovsky, Radu Calinescu, Ana Cavalcanti, Ibrahim Habli & Alan Thomas - 2022 - Minds and Machines 1:1-33.
    With recent advancements in systems engineering and artificial intelligence, autonomous agents are increasingly being called upon to execute tasks that have normative relevance. These are tasks that directly—and potentially adversely—affect human well-being and demand of the agent a degree of normative-sensitivity and -compliance. Such norms and normative principles are typically of a social, legal, ethical, empathetic, or cultural nature. Whereas norms of this type are often framed in the abstract, or as high-level principles, addressing normative concerns in concrete applications of (...)
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  • From Pluralistic Normative Principles to Autonomous-Agent Rules.Beverley Townsend, Colin Paterson, T. T. Arvind, Gabriel Nemirovsky, Radu Calinescu, Ana Cavalcanti, Ibrahim Habli & Alan Thomas - 2022 - Minds and Machines 32 (4):683-715.
    With recent advancements in systems engineering and artificial intelligence, autonomous agents are increasingly being called upon to execute tasks that have normative relevance. These are tasks that directly—and potentially adversely—affect human well-being and demand of the agent a degree of normative-sensitivity and -compliance. Such norms and normative principles are typically of a social, legal, ethical, empathetic, or cultural (‘SLEEC’) nature. Whereas norms of this type are often framed in the abstract, or as high-level principles, addressing normative concerns in concrete applications (...)
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  • Vivir con robots. Reflexiones éticas, jurídicas, sociales y culturales.Mario Toboso Martín & María Amparo Grau Ruiz - 2021 - Arbor 197 (802):a623.
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  • The Implications of Diverse Human Moral Foundations for Assessing the Ethicality of Artificial Intelligence.Jake B. Telkamp & Marc H. Anderson - 2022 - Journal of Business Ethics 178 (4):961-976.
    Organizations are making massive investments in artificial intelligence, and recent demonstrations and achievements highlight the immense potential for AI to improve organizational and human welfare. Yet realizing the potential of AI necessitates a better understanding of the various ethical issues involved with deciding to use AI, training and maintaining it, and allowing it to make decisions that have moral consequences. People want organizations using AI and the AI systems themselves to behave ethically, but ethical behavior means different things to different (...)
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  • Guidance systems: from autonomous directives to legal sensor-bilities.Simon M. Taylor & Marc De Leeuw - 2021 - AI and Society 36 (2):521-534.
    The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of ‘somatic surveillance’ increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication ‘a dissonance of attribution’ :7684, 2019. https://doi.org/10.1073/pnas.1805770115) (...)
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  • The Civic Role of Online Service Providers.Mariarosaria Taddeo - 2019 - Minds and Machines 29 (1):1-7.
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  • Actionable Principles for Artificial Intelligence Policy: Three Pathways.Charlotte Stix - 2021 - Science and Engineering Ethics 27 (1):1-17.
    In the development of governmental policy for artificial intelligence that is informed by ethics, one avenue currently pursued is that of drawing on “AI Ethics Principles”. However, these AI Ethics Principles often fail to be actioned in governmental policy. This paper proposes a novel framework for the development of ‘Actionable Principles for AI’. The approach acknowledges the relevance of AI Ethics Principles and homes in on methodological elements to increase their practical implementability in policy processes. As a case study, elements (...)
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  • Misplaced Trust and Distrust: How Not to Engage with Medical Artificial Intelligence.Georg Starke & Marcello Ienca - forthcoming - Cambridge Quarterly of Healthcare Ethics:1-10.
    Artificial intelligence (AI) plays a rapidly increasing role in clinical care. Many of these systems, for instance, deep learning-based applications using multilayered Artificial Neural Nets, exhibit epistemic opacity in the sense that they preclude comprehensive human understanding. In consequence, voices from industry, policymakers, and research have suggested trust as an attitude for engaging with clinical AI systems. Yet, in the philosophical and ethical literature on medical AI, the notion of trust remains fiercely debated. Trust skeptics hold that talking about trust (...)
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  • The Automated Laplacean Demon: How ML Challenges Our Views on Prediction and Explanation.Sanja Srećković, Andrea Berber & Nenad Filipović - 2021 - Minds and Machines 32 (1):159-183.
    Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what (...)
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  • The value of mass-digitised cultural heritage content in creative contexts.Chris Speed, Pip Thornton, Michael Smyth, Burkhard Schafer, Briana Pegado, Inge Panneels, Nicola Osborne, Susan Lechelt, Ingi Helgason, Chris Elsden, Steven Drost, Stephen Coleman & Melissa Terras - 2021 - Big Data and Society 8 (1).
    How can digitised assets of Galleries, Libraries, Archives and Museums be reused to unlock new value? What are the implications of viewing large-scale cultural heritage data as an economic resource, to build new products and services upon? Drawing upon valuation studies, we reflect on both the theory and practicalities of using mass-digitised heritage content as an economic driver, stressing the need to consider the complexity of commercial-based outcomes within the context of cultural and creative industries. However, we also problematise the (...)
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  • Ethical issues in computational pathology.Tom Sorell, Nasir Rajpoot & Clare Verrill - 2022 - Journal of Medical Ethics 48 (4):278-284.
    This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from the tension between artificial intelligence research—with its hunger for more and more data—and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; the fact that computational pathology lends itself to kinds (...)
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  • Consumers are willing to pay a price for explainable, but not for green AI. Evidence from a choice-based conjoint analysis.Markus B. Siewert, Stefan Wurster & Pascal D. König - 2022 - Big Data and Society 9 (1).
    A major challenge with the increasing use of Artificial Intelligence applications is to manage the long-term societal impacts of this technology. Two central concerns that have emerged in this respect are that the optimized goals behind the data processing of AI applications usually remain opaque and the energy footprint of their data processing is growing quickly. This study thus explores how much people value the transparency and environmental sustainability of AI using the example of personal AI assistants. The results from (...)
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  • Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling.Robert Shanklin, Michele Samorani, Shannon Harris & Michael A. Santoro - 2022 - Philosophy and Technology 35 (4):1-19.
    An Artificial Intelligence algorithm trained on data that reflect racial biases may yield racially biased outputs, even if the algorithm on its own is unbiased. For example, algorithms used to schedule medical appointments in the USA predict that Black patients are at a higher risk of no-show than non-Black patients, though technically accurate given existing data that prediction results in Black patients being overwhelmingly scheduled in appointment slots that cause longer wait times than non-Black patients. This perpetuates racial inequity, in (...)
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  • Economics of AI behavior: nudging the digital minds toward greater societal benefit.Emre Sezgin - forthcoming - AI and Society:1-2.
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  • In Defence of Principlism in AI Ethics and Governance.Elizabeth Seger - 2022 - Philosophy and Technology 35 (2):1-7.
    It is widely acknowledged that high-level AI principles are difficult to translate into practices via explicit rules and design guidelines. Consequently, many AI research and development groups that claim to adopt ethics principles have been accused of unwarranted “ethics washing”. Accordingly, there remains a question as to if and how high-level principles should be expected to influence the development of safe and beneficial AI. In this short commentary I discuss two roles high-level principles might play in AI ethics and governance. (...)
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  • From machine ethics to computational ethics.Samuel T. Segun - 2021 - AI and Society 36 (1):263-276.
    Research into the ethics of artificial intelligence is often categorized into two subareas—robot ethics and machine ethics. Many of the definitions and classifications of the subject matter of these subfields, as found in the literature, are conflated, which I seek to rectify. In this essay, I infer that using the term ‘machine ethics’ is too broad and glosses over issues that the term computational ethics best describes. I show that the subject of inquiry of computational ethics is of great value (...)
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  • Individual benefits and collective challenges: Experts’ views on data-driven approaches in medical research and healthcare in the German context.Silke Schicktanz & Lorina Buhr - 2022 - Big Data and Society 9 (1).
    Healthcare provision, like many other sectors of society, is undergoing major changes due to the increased use of data-driven methods and technologies. This increased reliance on big data in medicine can lead to shifts in the norms that guide healthcare providers and patients. Continuous critical normative reflection is called for to track such potential changes. This article presents the results of an interview-based study with 20 German and Swiss experts from the fields of medicine, life science research, informatics and humanities (...)
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  • The future of work: freedom, justice and capital in the age of artificial intelligence.Filippo Santoni de Sio, Txai Almeida & Jeroen van den Hoven - forthcoming - Critical Review of International Social and Political Philosophy.
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  • Sustainable biobanks: a case study for a green global bioethics.G. Samuel, F. Lucivero & A. M. Lucassen - 2022 - Global Bioethics 33 (1):50-64.
    This paper argues that as we move to redefine global bioethics, there is a need to be attentive to the ethical issues associated with the environmental sustainability of data and digital infrastructures in global health systems. We show that these infrastructures have thus far featured little in environmental impact discussions in the context of health, and we use a case study approach of biobanking to illustrate this. We argue that this missing discussion is problematic because biobanks have environmental impacts associated (...)
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  • Social Media and its Negative Impacts on Autonomy.Siavosh Sahebi & Paul Formosa - 2022 - Philosophy and Technology 35 (3):1-24.
    How social media impacts the autonomy of its users is a topic of increasing focus. However, much of the literature that explores these impacts fails to engage in depth with the philosophical literature on autonomy. This has resulted in a failure to consider the full range of impacts that social media might have on autonomy. A deeper consideration of these impacts is thus needed, given the importance of both autonomy as a moral concept and social media as a feature of (...)
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  • Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications.Mark Ryan & Bernd Carsten Stahl - 2021 - Journal of Information, Communication and Ethics in Society 19 (1):61-86.
    Purpose The purpose of this paper is clearly illustrate this convergence and the prescriptive recommendations that such documents entail. There is a significant amount of research into the ethical consequences of artificial intelligence. This is reflected by many outputs across academia, policy and the media. Many of these outputs aim to provide guidance to particular stakeholder groups. It has recently been shown that there is a large degree of convergence in terms of the principles upon which these guidance documents are (...)
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  • Detecting and explaining unfairness in consumer contracts through memory networks.Federico Ruggeri, Francesca Lagioia, Marco Lippi & Paolo Torroni - 2021 - Artificial Intelligence and Law 30 (1):59-92.
    Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the (...)
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  • “Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocation.Jon Rueda, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín & David Rodríguez-Arias - 2022 - AI and Society:1-12.
    The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps (...)
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  • AI and the expert; a blueprint for the ethical use of opaque AI.Amber Ross - forthcoming - AI and Society:1-12.
    The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...)
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  • Inteligencia artificial sostenible y evaluación ética constructiva.Antonio Luis Terrones Rodríguez - 2022 - Isegoría 67:10-10.
    El aumento considerable de la capacidad de la inteligencia artificial (IA) implica un alto consumo de recursos energéticos. La situación ambiental actual, caracterizada por la acuciante degradación de ecosistemas y la ruptura del equilibrio, exige tomar medidas en diversos ámbitos. La IA no puede quedar al margen, y aunque es empleada para objetivos de sostenibilidad, debe plantearse como sostenible en términos integrales. La propuesta de una inteligencia artificial sostenible se argumenta a partir de una evaluación ética constructiva, donde la inclusión (...)
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  • A Misdirected Principle with a Catch: Explicability for AI.Scott Robbins - 2019 - Minds and Machines 29 (4):495-514.
    There is widespread agreement that there should be a principle requiring that artificial intelligence be ‘explicable’. Microsoft, Google, the World Economic Forum, the draft AI ethics guidelines for the EU commission, etc. all include a principle for AI that falls under the umbrella of ‘explicability’. Roughly, the principle states that “for AI to promote and not constrain human autonomy, our ‘decision about who should decide’ must be informed by knowledge of how AI would act instead of us” :689–707, 2018). There (...)
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  • Achieving a ‘Good AI Society’: Comparing the Aims and Progress of the EU and the US.Huw Roberts, Josh Cowls, Emmie Hine, Francesca Mazzi, Andreas Tsamados, Mariarosaria Taddeo & Luciano Floridi - 2021 - Science and Engineering Ethics 27 (6):1-25.
    Over the past few years, there has been a proliferation of artificial intelligence strategies, released by governments around the world, that seek to maximise the benefits of AI and minimise potential harms. This article provides a comparative analysis of the European Union and the United States’ AI strategies and considers the visions of a ‘Good AI Society’ that are forwarded in key policy documents and their opportunity costs, the extent to which the implementation of each vision is living up to (...)
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  • Dual-Use and Trustworthy? A Mixed Methods Analysis of AI Diffusion Between Civilian and Defense R&D.Christian Reuter, Thea Riebe & Stefka Schmid - 2022 - Science and Engineering Ethics 28 (2):1-23.
    Artificial Intelligence (AI) seems to be impacting all industry sectors, while becoming a motor for innovation. The diffusion of AI from the civilian sector to the defense sector, and AI’s dual-use potential has drawn attention from security and ethics scholars. With the publication of the ethical guideline Trustworthy AI by the European Union (EU), normative questions on the application of AI have been further evaluated. In order to draw conclusions on Trustworthy AI as a point of reference for responsible research (...)
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  • No such thing as one-size-fits-all in AI ethics frameworks: a comparative case study.Vivian Qiang, Jimin Rhim & AJung Moon - forthcoming - AI and Society:1-20.
    Despite the bombardment of AI ethics frameworks (AIEFs) published in the last decade, it is unclear which of the many have been adopted in the industry. What is more, the sheer volume of AIEFs without a clear demonstration of their effectiveness makes it difficult for businesses to select which framework they should adopt. As a first step toward addressing this problem, we employed four different existing frameworks to assess AI ethics concerns of a real-world AI system. We compared the experience (...)
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  • Promoting responsible AI : A European perspective on the governance of artificial intelligence in media and journalism.Colin Porlezza - 2023 - Communications 48 (3):370-394.
    Artificial intelligence and automation have become pervasive in news media, influencing journalism from news gathering to news distribution. As algorithms are increasingly determining editorial decisions, specific concerns have been raised with regard to the responsible and accountable use of AI-driven tools by news media, encompassing new regulatory and ethical questions. This contribution aims to analyze whether and to what extent the use of AI technology in news media and journalism is currently regulated and debated within the European Union and the (...)
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  • Karl Jaspers and artificial neural nets: on the relation of explaining and understanding artificial intelligence in medicine.Christopher Poppe & Georg Starke - 2022 - Ethics and Information Technology 24 (3):1-10.
    Assistive systems based on Artificial Intelligence (AI) are bound to reshape decision-making in all areas of society. One of the most intricate challenges arising from their implementation in high-stakes environments such as medicine concerns their frequently unsatisfying levels of explainability, especially in the guise of the so-called black-box problem: highly successful models based on deep learning seem to be inherently opaque, resisting comprehensive explanations. This may explain why some scholars claim that research should focus on rendering AI systems understandable, rather (...)
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  • Ethical artificial intelligence framework for a good AI society: principles, opportunities and perils.Pradeep Paraman & Sanmugam Anamalah - 2023 - AI and Society 38 (2):595-611.
    The justification and rationality of this paper is to present some fundamental principles, theories, and concepts that we believe moulds the nucleus of a good artificial intelligence (AI) society. The morally accepted significance and utilitarian concerns that stems from the inception and realisation of an AI’s structural foundation are displayed in this study. This paper scrutinises the structural foundation, fundamentals, and cardinal righteous remonstrations, as well as the gaps in mechanisms towards novel prospects and perils in determining resilient fundamentals, accountability, (...)
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  • The political imaginary of National AI Strategies.Guy Paltieli - 2022 - AI and Society 37 (4):1613-1624.
    In the past few years, several democratic governments have published their National AI Strategies (NASs). These documents outline how AI technology should be implemented in the public sector and explain the policies that will ensure the ethical use of personal data. In this article, I examine these documents as political texts and reconstruct the political imaginary that underlies them. I argue that these documents intervene in contemporary democratic politics by suggesting that AI can help democracies overcome some of the challenges (...)
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