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  1. The tragedy of the AI commons.Travis LaCroix & Aydin Mohseni - 2022 - Synthese 200 (4):1-33.
    Policy and guideline proposals for ethical artificial intelligence research have proliferated in recent years. These are supposed to guide the socially-responsible development of AI for a common good. However, there typically exist incentives for non-cooperation ; and, these proposals often lack effective mechanisms to enforce their own normative claims. The situation just described constitutes a social dilemma—namely, a situation where no one has an individual incentive to cooperate, though mutual cooperation would lead to the best outcome for all involved. In (...)
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  • Operationalising AI ethics: barriers, enablers and next steps.Jessica Morley, Libby Kinsey, Anat Elhalal, Francesca Garcia, Marta Ziosi & Luciano Floridi - 2023 - AI and Society 38 (1):411-423.
    By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology (...)
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  • Ethics-based auditing of automated decision-making systems: intervention points and policy implications.Jakob Mökander & Maria Axente - 2023 - AI and Society 38 (1):153-171.
    Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA)—that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can (a) help organisations (...)
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  • Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model.Salvatore Sapienza & Anton Vedder - 2023 - AI and Society 38 (1):5-20.
    Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed (...)
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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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  • 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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  • Tradeoffs all the way down: Ethical abduction as a decision-making process for data-intensive technology development.Anissa Tanweer - 2022 - Big Data and Society 9 (1).
    Ample scholarship demonstrates that data-intensive technologies have the capacity to cause serious harm and that their developers are obliged to address ethics in their work. This ethnographic paper tells the story of data scientists attempting to instantiate a carefully considered ethical vision into a data infrastructure while balancing competing priorities, negotiating divergent interests, and wrestling with contrasting values. I use their story to develop the concept of “ethical abduction,” which I characterize as an exemplary process by which actors can intentionally (...)
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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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  • The teaching of computer ethics on computer science and related degree programmes. a European survey.Ioannis Stavrakakis, Damian Gordon, Brendan Tierney, Anna Becevel, Emma Murphy, Gordana Dodig-Crnkovic, Radu Dobrin, Viola Schiaffonati, Cristina Pereira, Svetlana Tikhonenko, J. Paul Gibson, Stephane Maag, Francesco Agresta, Andrea Curley, Michael Collins & Dympna O’Sullivan - 2021 - International Journal of Ethics Education 7 (1):101-129.
    Within the Computer Science community, many ethical issues have emerged as significant and critical concerns. Computer ethics is an academic field in its own right and there are unique ethical issues associated with information technology. It encompasses a range of issues and concerns including privacy and agency around personal information, Artificial Intelligence and pervasive technology, the Internet of Things and surveillance applications. As computing technology impacts society at an ever growing pace, there are growing calls for more computer ethics content (...)
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  • Organisational responses to the ethical issues of artificial intelligence.Bernd Carsten Stahl, Josephina Antoniou, Mark Ryan, Kevin Macnish & Tilimbe Jiya - 2022 - AI and Society 37 (1):23-37.
    The ethics of artificial intelligence is a widely discussed topic. There are numerous initiatives that aim to develop the principles and guidance to ensure that the development, deployment and use of AI are ethically acceptable. What is generally unclear is how organisations that make use of AI understand and address these ethical issues in practice. While there is an abundance of conceptual work on AI ethics, empirical insights are rare and often anecdotal. This paper fills the gap in our current (...)
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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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  • Transformation²: Making software engineering accountable for sustainability.Christoph Schneider & Stefanie Betz - 2022 - Journal of Responsible Technology 10:100027.
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  • Four investment areas for ethical AI: Transdisciplinary opportunities to close the publication-to-practice gap.Jana Schaich Borg - 2021 - Big Data and Society 8 (2).
    Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by (...)
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  • AI led ethical digital transformation: framework, research and managerial implications.Kumar Saurabh, Ridhi Arora, Neelam Rani, Debasisha Mishra & M. Ramkumar - 2022 - Journal of Information, Communication and Ethics in Society 20 (2):229-256.
    Purpose Digital transformation leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes. Artificial intelligence plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need (...)
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  • Connecting ethics and epistemology of AI.Federica Russo, Eric Schliesser & Jean Wagemans - forthcoming - AI and Society:1-19.
    The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate values and (...)
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  • Getting into the engine room: a blueprint to investigate the shadowy steps of AI ethics.Johan Rochel & Florian Evéquoz - 2021 - AI and Society 36 (2):609-622.
    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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  • Cultivating Moral Attention: a Virtue-Oriented Approach to Responsible Data Science in Healthcare.Emanuele Ratti & Mark Graves - 2021 - Philosophy and Technology 34 (4):1819-1846.
    In the past few years, the ethical ramifications of AI technologies have been at the center of intense debates. Considerable attention has been devoted to understanding how a morally responsible practice of data science can be promoted and which values have to shape it. In this context, ethics and moral responsibility have been mainly conceptualized as compliance to widely shared principles. However, several scholars have highlighted the limitations of such a principled approach. Drawing from microethics and the virtue theory tradition, (...)
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  • The limitation of ethics-based approaches to regulating artificial intelligence: regulatory gifting in the context of Russia.Gleb Papyshev & Masaru Yarime - forthcoming - AI and Society:1-16.
    The effects that artificial intelligence (AI) technologies will have on society in the short- and long-term are inherently uncertain. For this reason, many governments are avoiding strict command and control regulations for this technology and instead rely on softer ethics-based approaches. The Russian approach to regulating AI is characterized by the prevalence of unenforceable ethical principles implemented via industry self-regulation. We analyze the emergence of the regulatory regime for AI in Russia to illustrate the limitations of this approach. The article (...)
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  • Contextual Integrity as a General Conceptual Tool for Evaluating Technological Change.Elizabeth O’Neill - 2022 - Philosophy and Technology 35 (3):1-25.
    The fast pace of technological change necessitates new evaluative and deliberative tools. This article develops a general, functional approach to evaluating technological change, inspired by Nissenbaum’s theory of contextual integrity. Nissenbaum introduced the concept of contextual integrity to help analyze how technological changes can produce privacy problems. Reinterpreted, the concept of contextual integrity can aid our thinking about how technological changes affect the full range of human concerns and values—not only privacy. I propose a generalized concept of contextual integrity that (...)
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  • Ethics as a service: a pragmatic operationalisation of AI ethics.Jessica Morley, Anat Elhalal, Francesca Garcia, Libby Kinsey, Jakob Mökander & Luciano Floridi - 2021 - Minds and Machines 31 (2):239–256.
    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 (...)
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  • The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.Jakob Mökander, Margi Sheth, David S. Watson & Luciano Floridi - forthcoming - Minds and Machines:1-28.
    Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical (...)
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  • Ethics-based auditing of automated decision-making systems: nature, scope, and limitations.Jakob Mökander, Jessica Morley, Mariarosaria Taddeo & Luciano Floridi - 2021 - Science and Engineering Ethics 27 (4):1–30.
    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 (...)
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  • What about investors? ESG analyses as tools for ethics-based AI auditing.Matti Minkkinen, Anniina Niukkanen & Matti Mäntymäki - forthcoming - AI and Society:1-15.
    Artificial intelligence 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 governance (...)
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  • The ethics of algorithms from the perspective of the cultural history of consciousness: first look.Carlos Andres Salazar Martinez & Olga Lucia Quintero Montoya - forthcoming - AI and Society.
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  • AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind.Jocelyn Maclure - 2021 - Minds and Machines 31 (3):421-438.
    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 (...)
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  • Before and beyond trust: reliance in medical AI.Charalampia Kerasidou, Angeliki Kerasidou, Monika Buscher & Stephen Wilkinson - 2021 - Journal of Medical Ethics 48 (11):852-856.
    Artificial intelligence is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust (...)
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  • Employee Perceptions of the Effective Adoption of AI Principles.Stephanie Kelley - 2022 - Journal of Business Ethics 178 (4):871-893.
    This study examines employee perceptions on the effective adoption of artificial intelligence principles in their organizations. 49 interviews were conducted with employees of 24 organizations across 11 countries. Participants worked directly with AI across a range of positions, from junior data scientist to Chief Analytics Officer. The study found that there are eleven components that could impact the effective adoption of AI principles in organizations: communication, management support, training, an ethics office, a reporting mechanism, enforcement, measurement, accompanying technical processes, a (...)
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  • From Reality to World. A Critical Perspective on AI Fairness.Jean-Marie John-Mathews, Dominique Cardon & Christine Balagué - 2022 - Journal of Business Ethics 178 (4):945-959.
    Fairness of Artificial Intelligence decisions has become a big challenge for governments, companies, and societies. We offer a theoretical contribution to consider AI ethics outside of high-level and top-down approaches, based on the distinction between “reality” and “world” from Luc Boltanski. To do so, we provide a new perspective on the debate on AI fairness and show that criticism of ML unfairness is “realist”, in other words, grounded in an already instituted reality based on demographic categories produced by institutions. Second, (...)
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  • Operationalising AI ethics: how are companies bridging the gap between practice and principles? An exploratory study.Javier Camacho Ibáñez & Mónica Villas Olmeda - 2022 - AI and Society 37 (4):1663-1687.
    Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and two focus groups, (...)
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  • Achieving Equity with Predictive Policing Algorithms: A Social Safety Net Perspective.Chun-Ping Yen & Tzu-Wei Hung - 2021 - Science and Engineering Ethics 27 (3):1-16.
    Whereas using artificial intelligence to predict natural hazards is promising, applying a predictive policing algorithm to predict human threats to others continues to be debated. Whereas PPAs were reported to be initially successful in Germany and Japan, the killing of Black Americans by police in the US has sparked a call to dismantle AI in law enforcement. However, although PPAs may statistically associate suspects with economically disadvantaged classes and ethnic minorities, the targeted groups they aim to protect are often vulnerable (...)
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  • Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective.Erik Hermann - 2022 - Journal of Business Ethics 179 (1):43-61.
    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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  • Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability.Erik Hermann, Gunter Hermann & Jean-Christophe Tremblay - 2021 - Science and Engineering Ethics 27 (4):1-16.
    Artificial intelligence can be a game changer to address the global challenge of humanity-threatening climate change by fostering sustainable development. Since chemical research and development lay the foundation for innovative products and solutions, this study presents a novel chemical research and development process backed with artificial intelligence and guiding ethical principles to account for both process- and outcome-related sustainability. Particularly in ethically salient contexts, ethical principles have to accompany research and development powered by artificial intelligence to promote social and environmental (...)
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  • Beyond explainability: justifiability and contestability of algorithmic decision systems.Clément Henin & Daniel Le Métayer - 2022 - AI and Society 37 (4):1397-1410.
    In this paper, we point out that explainability is useful but not sufficient to ensure the legitimacy of algorithmic decision systems. We argue that the key requirements for high-stakes decision systems should be justifiability and contestability. We highlight the conceptual differences between explanations and justifications, provide dual definitions of justifications and contestations, and suggest different ways to operationalize justifiability and contestability.
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  • The Ethics of AI Ethics: An Evaluation of Guidelines.Thilo Hagendorff - 2020 - Minds and Machines 30 (1):99-120.
    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, (...)
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  • A Virtue-Based Framework to Support Putting AI Ethics into Practice.Thilo Hagendorff - 2022 - Philosophy and Technology 35 (3):1-24.
    Many ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, several AI ethics researchers have pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach. This paper proposes a complementary to the principled approach that is based on virtue ethics. It defines four “basic AI virtues”, namely justice, honesty, responsibility and care, all of which represent specific (...)
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  • Apprehending AI moral purpose in practical wisdom.Mark Graves - 2022 - AI and Society:1-14.
    Practical wisdom enables moral decision-making and action by aligning one’s apprehension of proximate goods with a distal, socially embedded interpretation of a more ultimate Good. A focus on purpose within the overall process mutually informs human moral psychology and moral AI development in their examinations of practical wisdom. AI practical wisdom could ground an AI system’s apprehension of reality in a sociotechnical moral process committed to orienting AI development and action in light of a pluralistic, diverse interpretation of that Good. (...)
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  • Ethics in the Software Development Process: from Codes of Conduct to Ethical Deliberation.Jan Gogoll, Niina Zuber, Severin Kacianka, Timo Greger, Alexander Pretschner & Julian Nida-Rümelin - 2021 - Philosophy and Technology 34 (4):1085-1108.
    Software systems play an ever more important role in our lives and software engineers and their companies find themselves in a position where they are held responsible for ethical issues that may arise. In this paper, we try to disentangle ethical considerations that can be performed at the level of the software engineer from those that belong in the wider domain of business ethics. The handling of ethical problems that fall into the responsibility of the engineer has traditionally been addressed (...)
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  • An exploratory qualitative analysis of AI ethics guidelines.Aline Shakti Franzke - 2022 - Journal of Information, Communication and Ethics in Society 20 (4):401-423.
    Purpose As Big Data and Artificial Intelligence (AI) proliferate, calls have emerged for ethical reflection. Ethics guidelines have played a central role in this respect. While quantitative research on the ethics guidelines of AI/Big Data has been undertaken, there has been a dearth of systematic qualitative analyses of these documents. Design/methodology/approach Aiming to address this research gap, this paper analyses 70 international ethics guidelines documents from academia, NGOs and the corporate realm, published between 2017 and 2020. Findings The article presents (...)
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  • Investing in AI for social good: an analysis of European national strategies.Francesca Foffano, Teresa Scantamburlo & Atia Cortés - forthcoming - AI and Society.
    Artificial Intelligence has become a driving force in modern research, industry and public administration and the European Union is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good of society through (...)
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  • On the Contribution of Neuroethics to the Ethics and Regulation of Artificial Intelligence.Michele Farisco, Kathinka Evers & Arleen Salles - 2022 - Neuroethics 15 (1):1-12.
    Contemporary ethical analysis of Artificial Intelligence is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, (...)
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  • Implementing Ethics in Healthcare AI-Based Applications: A Scoping Review.Robyn Clay-Williams, Elizabeth Austin & Magali Goirand - 2021 - Science and Engineering Ethics 27 (5):1-53.
    A number of Artificial Intelligence ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the adoption of AI in different sectors, including healthcare. While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications are challenging the existing ethics and regulatory frameworks. This scoping review explores how ethics frameworks have been implemented in AIHA, how these implementations have been evaluated and whether they have been successful. AI specific (...)
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  • Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence.Alexander Buhmann & Christian Fieseler - forthcoming - Business Ethics Quarterly:1-34.
    Responsible innovation in artificial intelligence calls for public deliberation: well-informed “deep democratic” debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge of (...)
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  • From the Ground Truth Up: Doing AI Ethics from Practice to Principles.James Brusseau - 2022 - AI and Society 37 (1):1-7.
    Recent AI ethics has focused on applying abstract principles downward to practice. This paper moves in the other direction. Ethical insights are generated from the lived experiences of AI-designers working on tangible human problems, and then cycled upward to influence theoretical debates surrounding these questions: 1) Should AI as trustworthy be sought through explainability, or accurate performance? 2) Should AI be considered trustworthy at all, or is reliability a preferable aim? 3) Should AI ethics be oriented toward establishing protections for (...)
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  • Artificial intelligence ethics has a black box problem.Jean-Christophe Bélisle-Pipon, Erica Monteferrante, Marie-Christine Roy & Vincent Couture - forthcoming - AI and Society:1-16.
    It has become a truism that the ethics of artificial intelligence is necessary and must help guide technological developments. Numerous ethical guidelines have emerged from academia, industry, government and civil society in recent years. While they provide a basis for discussion on appropriate regulation of AI, it is not always clear how these ethical guidelines were developed, and by whom. Using content analysis, we surveyed a sample of the major documents and analyzed the accessible information regarding their methodology and stakeholder (...)
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  • On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning.Justin B. Biddle - 2022 - Canadian Journal of Philosophy 52 (3):321-341.
    Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems (...)
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  • What does it mean to embed ethics in data science? An integrative approach based on the microethics and virtues.Louise Bezuidenhout & Emanuele Ratti - 2021 - AI and Society 36:939–953.
    In the past few years, scholars have been questioning whether the current approach in data ethics based on the higher level case studies and general principles is effective. In particular, some have been complaining that such an approach to ethics is difficult to be applied and to be taught in the context of data science. In response to these concerns, there have been discussions about how ethics should be “embedded” in the practice of data science, in the sense of showing (...)
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  • Characteristics and challenges in the industries towards responsible AI: a systematic literature review.Marianna Anagnostou, Olga Karvounidou, Chrysovalantou Katritzidaki, Christina Kechagia, Kyriaki Melidou, Eleni Mpeza, Ioannis Konstantinidis, Eleni Kapantai, Christos Berberidis, Ioannis Magnisalis & Vassilios Peristeras - 2022 - Ethics and Information Technology 24 (3):1-18.
    Today humanity is in the midst of the massive expansion of new and fundamental technology, represented by advanced artificial intelligence (AI) systems. The ongoing revolution of these technologies and their profound impact across various sectors, has triggered discussions about the characteristics and values that should guide their use and development in a responsible manner. In this paper, we conduct a systematic literature review with the aim of pointing out existing challenges and required principles in AI-based systems in different industries. We (...)
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  • The Future of Value Sensitive Design.Batya Friedman, David Hendry, Steven Umbrello, Jeroen Van Den Hoven & Daisy Yoo - 2020 - Paradigm Shifts in ICT Ethics: Proceedings of the 18th International Conference ETHICOMP 2020.
    In this panel, we explore the future of value sensitive design (VSD). The stakes are high. Many in public and private sectors and in civil society are gradually realizing that taking our values seriously implies that we have to ensure that values effectively inform the design of technology which, in turn, shapes people’s lives. Value sensitive design offers a highly developed set of theory, tools, and methods to systematically do so.
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  • Microethics for healthcare data science: attention to capabilities in sociotechnical systems.Mark Graves & Emanuele Ratti - 2021 - The Future of Science and Ethics 6:64-73.
    It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare as an example, (...)
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