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  1. Effective Human Oversight of AI-Based Systems: A Signal Detection Perspective on the Detection of Inaccurate and Unfair Outputs.Markus Langer, Kevin Baum & Nadine Schlicker - 2024 - Minds and Machines 35 (1):1-30.
    Legislation and ethical guidelines around the globe call for effective human oversight of AI-based systems in high-risk contexts – that is oversight that reliably reduces the risks otherwise associated with the use of AI-based systems. Such risks may relate to the imperfect accuracy of systems (e.g., inaccurate classifications) or to ethical concerns (e.g., unfairness of outputs). Given the significant role that human oversight is expected to play in the operation of AI-based systems, it is crucial to better understand the conditions (...)
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  • Attention, Moral Skill, and Algorithmic Recommendation.Nick Schuster & Seth Lazar - forthcoming - Philosophical Studies.
    Recommender systems are artificial intelligence technologies, deployed by online platforms, that model our individual preferences and direct our attention to content we’re likely to engage with. As the digital world has become increasingly saturated with information, we’ve become ever more reliant on these tools to efficiently allocate our attention. And our reliance on algorithmic recommendation may, in turn, reshape us as moral agents. While recommender systems could in principle enhance our moral agency by enabling us to cut through the information (...)
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  • Ethics of Artificial Intelligence.Stefan Buijsman, Michael Klenk & Jeroen van den Hoven - forthcoming - In Nathalie Smuha (ed.), Cambridge Handbook on the Law, Ethics and Policy of AI. Cambridge University Press.
    Artificial Intelligence (AI) is increasingly adopted in society, creating numerous opportunities but at the same time posing ethical challenges. Many of these are familiar, such as issues of fairness, responsibility and privacy, but are presented in a new and challenging guise due to our limited ability to steer and predict the outputs of AI systems. This chapter first introduces these ethical challenges, stressing that overviews of values are a good starting point but frequently fail to suffice due to the context (...)
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  • AI content detection in the emerging information ecosystem: new obligations for media and tech companies.Alistair Knott, Dino Pedreschi, Toshiya Jitsuzumi, Susan Leavy, David Eyers, Tapabrata Chakraborti, Andrew Trotman, Sundar Sundareswaran, Ricardo Baeza-Yates, Przemyslaw Biecek, Adrian Weller, Paul D. Teal, Subhadip Basu, Mehmet Haklidir, Virginia Morini, Stuart Russell & Yoshua Bengio - 2024 - Ethics and Information Technology 26 (4):1-14.
    The world is about to be swamped by an unprecedented wave of AI-generated content. We need reliable ways of identifying such content, to supplement the many existing social institutions that enable trust between people and organisations and ensure social resilience. In this paper, we begin by highlighting an important new development: providers of AI content generators have new obligations to support the creation of reliable detectors for the content they generate. These new obligations arise mainly from the EU’s newly finalised (...)
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  • Transparency and its roles in realizing greener AI.Omoregie Charles Osifo - 2023 - Journal of Information, Communication and Ethics in Society 21 (2):202-218.
    Purpose The purpose of this paper is to identify the key roles of transparency in making artificial intelligence (AI) greener (i.e. causing lesser carbon dioxide emissions) during the design, development and manufacturing stages or processes of AI technologies (e.g. apps, systems, agents, tools, artifacts) and use the “explicability requirement” as an essential value within the framework of transparency in supporting arguments for realizing greener AI. Design/methodology/approach The approach of this paper is argumentative, which is supported by ideas from existing literature (...)
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  • A metaphysical account of agency for technology governance.Sadjad Soltanzadeh - forthcoming - AI and Society:1-12.
    The way in which agency is conceptualised has implications for understanding human–machine interactions and the governance of technology, especially artificial intelligence (AI) systems. Traditionally, agency is conceptualised as a capacity, defined by intrinsic properties, such as cognitive or volitional facilities. I argue that the capacity-based account of agency is inadequate to explain the dynamics of human–machine interactions and guide technology governance. Instead, I propose to conceptualise agency as impact. Agents as impactful entities can be identified at different levels: from the (...)
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  • The decision-point-dilemma: Yet another problem of responsibility in human-AI interaction.Laura Crompton - 2021 - Journal of Responsible Technology 7:100013.
    AI as decision support supposedly helps human agents make ‘better’decisions more efficiently. However, research shows that it can, sometimes greatly, influence the decisions of its human users. While there has been a fair amount of research on intended AI influence, there seem to be great gaps within both theoretical and practical studies concerning unintended AI influence. In this paper I aim to address some of these gaps, and hope to shed some light on the ethical and moral concerns that arise (...)
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  • Chatbot breakthrough in the 2020s? An ethical reflection on the trend of automated consultations in health care.Jaana Parviainen & Juho Rantala - 2022 - Medicine, Health Care and Philosophy 25 (1):61-71.
    Many experts have emphasised that chatbots are not sufficiently mature to be able to technically diagnose patient conditions or replace the judgements of health professionals. The COVID-19 pandemic, however, has significantly increased the utilisation of health-oriented chatbots, for instance, as a conversational interface to answer questions, recommend care options, check symptoms and complete tasks such as booking appointments. In this paper, we take a proactive approach and consider how the emergence of task-oriented chatbots as partially automated consulting systems can influence (...)
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  • Social impacts of algorithmic decision-making: A research agenda for the social sciences.Frauke Kreuter, Christoph Kern, Ruben L. Bach & Frederic Gerdon - 2022 - Big Data and Society 9 (1).
    Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data (...)
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  • Conceptualizing Automated Decision-Making in Organizational Contexts.Anna Katharina Boos - 2024 - Philosophy and Technology 37 (3):1-30.
    Despite growing interest in automated (or algorithmic) decision-making (ADM), little work has been done to conceptually clarify the term. This article aims to tackle this issue by developing a conceptualization of ADM specifically tailored to organizational contexts. It has two main goals: (1) to meaningfully demarcate ADM from similar, yet distinct algorithm-supported practices; and (2) to draw internal distinctions such that different ADM types can be meaningfully distinguished. The proposed conceptualization builds on three arguments: First, ADM primarily refers to the (...)
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  • “Computer says no”: Algorithmic decision support and organisational responsibility.Angelika Adensamer, Rita Gsenger & Lukas Daniel Klausner - 2021 - Journal of Responsible Technology 7-8 (C):100014.
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