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  1. Is Epistemic Autonomy Technologically Possible Within Social Media? A Socio-Epistemological Investigation of the Epistemic Opacity of Social Media Platforms.Margherita Mattioni - forthcoming - Topoi:1-14.
    This article aims to provide a coherent and comprehensive theoretical framework of the main socio-epistemic features of social media. The first part consists of a concise discussion of the main epistemic consequences of personalised information filtering, with a focus on echo chambers and their many different implications. The middle section instead hosts an analytical investigation of the cognitive and epistemic environments of these platforms aimed at establishing whether, and to what extent, they allow their users to be epistemically vigilant with (...)
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  • Mapping the landscape of ethical considerations in explainable AI research.Luca Nannini, Marta Marchiori Manerba & Isacco Beretta - 2024 - Ethics and Information Technology 26 (3):1-22.
    With its potential to contribute to the ethical governance of AI, eXplainable AI (XAI) research frequently asserts its relevance to ethical considerations. Yet, the substantiation of these claims with rigorous ethical analysis and reflection remains largely unexamined. This contribution endeavors to scrutinize the relationship between XAI and ethical considerations. By systematically reviewing research papers mentioning ethical terms in XAI frameworks and tools, we investigate the extent and depth of ethical discussions in scholarly research. We observe a limited and often superficial (...)
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  • Intellectual Virtues and Scientific Endeavor: A Reflection on the Commitments Inherent in Generating and Possessing Knowledge.Oscar Eliezer Mendoza-De Los Santos - 2023 - Bulletin of Science, Technology and Society 43 (1-2):18-31.
    In this essay, I reflect on the implications of intellectual virtues in scientific endeavor. To this end, I first offer a depiction of scientific endeavor by resorting to the notion of academic attitude, which involves aspects concerning the generation and possession of knowledge. Although there are differences between these activities, they have in common the engagement of diverse intellectual agents (scientists). In this sense, I analyze how intellectual virtues are linked to 1) scientific research tasks, such as theory appraisal, and (...)
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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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  • 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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  • The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?Jakob Mökander, Prathm Juneja, David S. Watson & Luciano Floridi - 2022 - Minds and Machines 32 (4):751-758.
    On the whole, the US Algorithmic Accountability Act of 2022 (US AAA) is a pragmatic approach to balancing the benefits and risks of automated decision systems. Yet there is still room for improvement. This commentary highlights how the US AAA can both inform and learn from the European Artificial Intelligence Act (EU AIA).
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  • Accountability in Artificial Intelligence: What It Is and How It Works.Claudio Novelli, Mariarosaria Taddeo & Luciano Floridi - 2023 - AI and Society 1:1-12.
    Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...)
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  • Accepting Moral Responsibility for the Actions of Autonomous Weapons Systems—a Moral Gambit.Mariarosaria Taddeo & Alexander Blanchard - 2022 - Philosophy and Technology 35 (3):1-24.
    In this article, we focus on the attribution of moral responsibility for the actions of autonomous weapons systems (AWS). To do so, we suggest that the responsibility gap can be closed if human agents can take meaningful moral responsibility for the actions of AWS. This is a moral responsibility attributed to individuals in a justified and fair way and which is accepted by individuals as an assessment of their own moral character. We argue that, given the unpredictability of AWS, meaningful (...)
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  • Artificial intelligence and work: a critical review of recent research from the social sciences.Jean-Philippe Deranty & Thomas Corbin - forthcoming - AI and Society:1-17.
    This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative and (...)
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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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  • Weapons of moral construction? On the value of fairness in algorithmic decision-making.Simona Tiribelli & Benedetta Giovanola - 2022 - Ethics and Information Technology 24 (1):1-13.
    Fairness is one of the most prominent values in the Ethics and Artificial Intelligence (AI) debate and, specifically, in the discussion on algorithmic decision-making (ADM). However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed (...)
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  • Speeding up to keep up: exploring the use of AI in the research process.Jennifer Chubb, Peter Cowling & Darren Reed - 2022 - AI and Society 37 (4):1439-1457.
    There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to (...)
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  • Steering Representations—Towards a Critical Understanding of Digital Twins.Paulan Korenhof, Vincent Blok & Sanneke Kloppenburg - 2021 - Philosophy and Technology 34 (4):1751-1773.
    Digital Twins are conceptualised in the academic technical discourse as real-time realistic digital representations of physical entities. Originating from product engineering, the Digital Twin quickly advanced into other fields, including the life sciences and earth sciences. Digital Twins are seen by the tech sector as the new promising tool for efficiency and optimisation, while governmental agencies see it as a fruitful means for improving decision-making to meet sustainability goals. A striking example of the latter is the European Commission who wishes (...)
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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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  • Trustworthy AI: AI made in Germany and Europe?Hartmut Hirsch-Kreinsen & Thorben Krokowski - forthcoming - AI and Society:1-11.
    As the capabilities of artificial intelligence (AI) continue to expand, concerns are also growing about the ethical and social consequences of unregulated development and, above all, use of AI systems in a wide range of social areas. It is therefore indisputable that the application of AI requires social standardization and regulation. For years, innovation policy measures and the most diverse activities of European and German institutions have been directed toward this goal. Under the label “Trustworthy AI” (TAI), a promise is (...)
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  • Artificial Intelligence in medicine: reshaping the face of medical practice.Max Tretter, David Samhammer & Peter Dabrock - 2023 - Ethik in der Medizin 36 (1):7-29.
    Background The use of Artificial Intelligence (AI) has the potential to provide relief in the challenging and often stressful clinical setting for physicians. So far, however, the actual changes in work for physicians remain a prediction for the future, including new demands on the social level of medical practice. Thus, the question of how the requirements for physicians will change due to the implementation of AI is addressed. Methods The question is approached through conceptual considerations based on the potentials that (...)
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  • The latent space of data ethics.Enrico Panai - forthcoming - AI and Society:1-19.
    In informationally mature societies, almost all organisations record, generate, process, use, share and disseminate data. In particular, the rise of AI and autonomous systems has corresponded to an improvement in computational power and in solving complex problems. However, the resulting possibilities have been coupled with an upsurge of ethical risks. To avoid the misuse, underuse, and harmful use of data and data-based systems like AI, we should use an ethical framework appropriate to the object of its reasoning. Unfortunately, in recent (...)
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  • Hiring, Algorithms, and Choice: Why Interviews Still Matter.Vikram R. Bhargava & Pooria Assadi - 2024 - Business Ethics Quarterly 34 (2):201-230.
    Why do organizations conduct job interviews? The traditional view of interviewing holds that interviews are conducted, despite their steep costs, to predict a candidate’s future performance and fit. This view faces a twofold threat: the behavioral and algorithmic threats. Specifically, an overwhelming body of behavioral research suggests that we are bad at predicting performance and fit; furthermore, algorithms are already better than us at making these predictions in various domains. If the traditional view captures the whole story, then interviews seem (...)
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  • Decolonizing AI Ethics: Relational Autonomy as a Means to Counter AI Harms.Sábëlo Mhlambi & Simona Tiribelli - 2023 - Topoi 42 (3):867-880.
    Many popular artificial intelligence (AI) ethics frameworks center the principle of autonomy as necessary in order to mitigate the harms that might result from the use of AI within society. These harms often disproportionately affect the most marginalized within society. In this paper, we argue that the principle of autonomy, as currently formalized in AI ethics, is itself flawed, as it expresses only a mainstream mainly liberal notion of autonomy as rational self-determination, derived from Western traditional philosophy. In particular, we (...)
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  • (1 other version)The epistemological foundations of data science: a critical review.Luciano Floridi, Mariarosaria Taddeo, Vincent Wang, David Watson & Jules Desai - 2022 - Synthese 200 (6):1-27.
    The modern abundance and prominence of data have led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...)
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  • The paradox of the artificial intelligence system development process: the use case of corporate wellness programs using smart wearables.Alessandra Angelucci, Ziyue Li, Niya Stoimenova & Stefano Canali - forthcoming - AI and Society:1-11.
    Artificial intelligence systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will function. This means that (...)
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  • Bias in algorithms of AI systems developed for COVID-19: A scoping review.Janet Delgado, Alicia de Manuel, Iris Parra, Cristian Moyano, Jon Rueda, Ariel Guersenzvaig, Txetxu Ausin, Maite Cruz, David Casacuberta & Angel Puyol - 2022 - Journal of Bioethical Inquiry 19 (3):407-419.
    To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact (...)
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  • Are AI systems biased against the poor? A machine learning analysis using Word2Vec and GloVe embeddings.Georgina Curto, Mario Fernando Jojoa Acosta, Flavio Comim & Begoña Garcia-Zapirain - forthcoming - AI and Society:1-16.
    Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the (...)
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  • Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms.Benedetta Giovanola & Simona Tiribelli - 2023 - AI and Society 38 (2):549-563.
    The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently (...)
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  • Artificial intelligence in support of the circular economy: ethical considerations and a path forward.Huw Roberts, Joyce Zhang, Ben Bariach, Josh Cowls, Ben Gilburt, Prathm Juneja, Andreas Tsamados, Marta Ziosi, Mariarosaria Taddeo & Luciano Floridi - forthcoming - AI and Society:1-14.
    The world’s current model for economic development is unsustainable. It encourages high levels of resource extraction, consumption, and waste that undermine positive environmental outcomes. Transitioning to a circular economy (CE) model of development has been proposed as a sustainable alternative. Artificial intelligence (AI) is a crucial enabler for CE. It can aid in designing robust and sustainable products, facilitate new circular business models, and support the broader infrastructures needed to scale circularity. However, to date, considerations of the ethical implications of (...)
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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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  • Ethical governance of artificial intelligence for defence: normative tradeoffs for principle to practice guidance.Alexander Blanchard, Christopher Thomas & Mariarosaria Taddeo - forthcoming - AI and Society:1-14.
    The rapid diffusion of artificial intelligence (AI) technologies in the defence domain raises challenges for the ethical governance of these systems. A recent shift from the what to the how of AI ethics sees a nascent body of literature published by defence organisations focussed on guidance to implement AI ethics principles. These efforts have neglected a crucial intermediate step between principles and guidance concerning the elicitation of ethical requirements for specifying the guidance. In this article, we outline the key normative (...)
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  • The Right to be an Exception to Predictions: a Moral Defense of Diversity in Recommendation Systems.Eleonora Viganò - 2023 - Philosophy and Technology 36 (3):1-25.
    Recommendation systems (RSs) predict what the user likes and recommend it to them. While at the onset of RSs, the latter was designed to maximize the recommendation accuracy (i.e., accuracy was their only goal), nowadays many RSs models include diversity in recommendations (which thus is a further goal of RSs). In the computer science community, the introduction of diversity in RSs is justified mainly through economic reasons: diversity increases user satisfaction and, in niche markets, profits.I contend that, first, the economic (...)
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  • Introduction: Digital Technologies and Human Decision-Making.Sofia Bonicalzi, Mario De Caro & Benedetta Giovanola - 2023 - Topoi 42 (3):793-797.
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  • Why Should We Care About the Manipulative Power of Algorithmic Transparency?Hao Wang - 2023 - Philosophy and Technology 36 (1):1-6.
    Franke Philosophy & Technology, 35(4), 1-7, (2022) offers an interesting claim that algorithmic transparency as manipulation does not necessarily follow that it is good or bad. Different people can have good reasons to adopt different evaluative attitudes towards this manipulation. Despite agreeing with some of his observations, this short reply will examine three crucial misconceptions in his arguments. In doing so, it defends why we are morally obliged to care about the manipulative potential of algorithmic transparency. It suggests that we (...)
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  • Introduction to the Special Issue on Intercultural Digital Ethics.Nikita Aggarwal - 2020 - Philosophy and Technology 33 (4):547-550.
    Recent advances in the capability of digital information technologies—particularly due to advances in artificial intelligence —have invigorated the debate on the ethical issues surrounding their use. However, this debate has often been dominated by ‘Western’ ethical perspectives, values and interests, to the exclusion of broader ethical and socio-cultural perspectives. This imbalance carries the risk that digital technologies produce ethical harms and lack social acceptance, when the ethical norms and values designed into these technologies collide with those of the communities in (...)
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  • Health professions students’ perceptions of artificial intelligence and its integration to health professions education and healthcare: a thematic analysis.Ejercito Mangawa Balay-Odao, Dinara Omirzakova, Srinivasa Rao Bolla, Joseph U. Almazan & Jonas Preposi Cruz - forthcoming - AI and Society:1-11.
    Artificial intelligence (AI) is being tightly integrated into healthcare today. Even though AI is being utilized in healthcare, its application in clinical settings and health professions education is still controversial. The study described the perceptions of AI and its integration into health professions education and healthcare among health professions students. This descriptive phenomenological study analyzed the data from a purposive sample of 33 health professions students at a university in Kazakhstan using the thematic approach. Data collection was conducted from March (...)
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  • The landscape of data and AI documentation approaches in the European policy context.Josep Soler-Garrido, Blagoj Delipetrev, Isabelle Hupont & Marina Micheli - 2023 - Ethics and Information Technology 25 (4):1-21.
    Nowadays, Artificial Intelligence (AI) is present in all sectors of the economy. Consequently, both data-the raw material used to build AI systems- and AI have an unprecedented impact on society and there is a need to ensure that they work for its benefit. For this reason, the European Union has put data and trustworthy AI at the center of recent legislative initiatives. An important element in these regulations is transparency, understood as the provision of information to relevant stakeholders to support (...)
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  • Artificial Intelligent Systems and Ethical Agency.Reena Cheruvalath - 2023 - Journal of Human Values 29 (1):33-47.
    The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective nature of ethical decision-making. (...)
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  • AI ageism: a critical roadmap for studying age discrimination and exclusion in digitalized societies.Justyna Stypinska - 2023 - AI and Society 38 (2):665-677.
    In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism (...)
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  • Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs).David Steingard, Marcello Balduccini & Akanksha Sinha - 2023 - AI and Society 38 (2):613-629.
    This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to (...)
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  • Ethics of Quantum Computing: an Outline.Luca M. Possati - 2023 - Philosophy and Technology 36 (3):1-21.
    This paper intends to contribute to the emerging literature on the ethical problems posed by quantum computing and quantum technologies in general. The key ethical questions are as follows: Does quantum computing pose new ethical problems, or are those raised by quantum computing just a different version of the same ethical problems raised by other technologies, such as nanotechnologies, nuclear plants, or cloud computing? In other words, what is new in quantum computing from an ethical point of view? The paper (...)
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  • Automating anticorruption?María Carolina Jiménez & Emanuela Ceva - 2022 - Ethics and Information Technology 24 (4):1-14.
    The paper explores some normative challenges concerning the integration of Machine Learning (ML) algorithms into anticorruption in public institutions. The challenges emerge from the tensions between an approach treating ML algorithms as allies to an exclusively legalistic conception of anticorruption and an approach seeing them within an institutional ethics of office accountability. We explore two main challenges. One concerns the variable opacity of some ML algorithms, which may affect public officeholders’ capacity to account for institutional processes relying upon ML techniques. (...)
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  • Promises and Pitfalls of Algorithm Use by State Authorities.Maryam Amir Haeri, Kathrin Hartmann, Jürgen Sirsch, Georg Wenzelburger & Katharina A. Zweig - 2022 - Philosophy and Technology 35 (2):1-31.
    Algorithmic systems are increasingly used by state agencies to inform decisions about humans. They produce scores on risks of recidivism in criminal justice, indicate the probability for a job seeker to find a job in the labor market, or calculate whether an applicant should get access to a certain university program. In this contribution, we take an interdisciplinary perspective, provide a bird’s eye view of the different key decisions that are to be taken when state actors decide to use an (...)
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  • Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship.Florian Funer - 2022 - Philosophy and Technology 35 (1):1-20.
    The initial successes in recent years in harnessing machine learning technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as (...)
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  • AI, big data, and the future of consent.Adam J. Andreotta, Nin Kirkham & Marco Rizzi - 2022 - AI and Society 37 (4):1715-1728.
    In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can impede (...)
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  • Watch out! Cities as data engines.Fabio Duarte & Barbro Fröding - 2023 - AI and Society 38 (3):1249-1250.
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  • SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development.Georgina Curto & Flavio Comim - 2023 - Science and Engineering Ethics 29 (4):1-19.
    This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process (...)
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  • Tensions in transparent urban AI: designing a smart electric vehicle charge point.Kars Alfrink, Ianus Keller, Neelke Doorn & Gerd Kortuem - 2023 - AI and Society 38 (3):1049-1065.
    The increasing use of artificial intelligence (AI) by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent (...)
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