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  1. 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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  • 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. To do so, we suggest that the responsibility gap can be closed if human agents can take meaningful moral responsibility for the actions of AWS. This is a moral responsibility attributed to individuals in a justified and fair way and which is accepted by individuals as an assessment of their own moral character. We argue that, given the unpredictability of AWS, meaningful moral (...)
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  • Artificial Intelligent Systems and Ethical Agency.Reena Cheruvalath - forthcoming - Journal of Human Values:097168582211195.
    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: programming ethical guidance in the artificial Intelligence -equipped machines and/or 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. The challenge (...)
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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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  • Tensions in transparent urban AI: designing a smart electric vehicle charge point.Kars Alfrink, Ianus Keller, Neelke Doorn & Gerd Kortuem - forthcoming - AI and Society:1-17.
    The increasing use of artificial intelligence 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 smart (...)
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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 - forthcoming - Minds and Machines:1-8.
    On the whole, the US Algorithmic Accountability Act of 2022 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.
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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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  • 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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  • Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals.David Steingard, Marcello Balduccini & Akanksha Sinha - forthcoming - AI and Society:1-17.
    This paper offers three contributions to the burgeoning movements of AI for Social Good and AI and the United Nations Sustainable Development Goals. First, we introduce the SDG-Intense Evaluation framework 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 the SDGs. Second, (...)
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  • Beyond Bias and Discrimination: Redefining the AI Ethics Principle of Fairness in Healthcare Machine-Learning Algorithms.Benedetta Giovanola & Simona Tiribelli - forthcoming - AI and Society.
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  • Watch Out! Cities as Data Engines.Fabio Duarte & Barbro Fröding - forthcoming - AI and Society.
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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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  • 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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  • Weapons of Moral Construction? On the Value of Fairness in Algorithmic Decision-Making.Simona Tiribelli & Benedetta Giovanola - 2022 - Ethics and Information Technology 24 (1).
    Fairness is one of the most prominent values in the Ethics and Artificial Intelligence debate and, specifically, in the discussion on algorithmic decision-making. 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 at clarifying (...)
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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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  • 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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  • 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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  • 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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