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  1. The Democratic Inclusion of Artificial Intelligence? Exploring the Patiency, Agency and Relational Conditions for Demos Membership.Ludvig Beckman & Jonas Hultin Rosenberg - 2022 - Philosophy and Technology 35 (2):1-24.
    Should artificial intelligences ever be included as co-authors of democratic decisions? According to the conventional view in democratic theory, the answer depends on the relationship between the political unit and the entity that is either affected or subjected to its decisions. The relational conditions for inclusion as stipulated by the all-affected and all-subjected principles determine the spatial extension of democratic inclusion. Thus, AI qualifies for democratic inclusion if and only if AI is either affected or subjected to decisions by the (...)
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  • Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments.Ahmed Izzidien - 2022 - AI and Society 37 (1):299-318.
    Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing (...)
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  • Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - forthcoming - American Journal of Bioethics:1-17.
    Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...)
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  • Computer Says I Don’t Know: An Empirical Approach to Capture Moral Uncertainty in Artificial Intelligence.Andreia Martinho, Maarten Kroesen & Caspar Chorus - 2021 - Minds and Machines 31 (2):215-237.
    As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of (...)
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  • Embedding Values in Artificial Intelligence (AI) Systems.Ibo van de Poel - 2020 - Minds and Machines 30 (3):385-409.
    Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and values that should be adhered to in the design and deployment of artificial intelligence. These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody certain values. (...)
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  • Moral Gridworlds: A Theoretical Proposal for Modeling Artificial Moral Cognition.Julia Haas - 2020 - Minds and Machines 30 (2):219-246.
    I describe a suite of reinforcement learning environments in which artificial agents learn to value and respond to moral content and contexts. I illustrate the core principles of the framework by characterizing one such environment, or “gridworld,” in which an agent learns to trade-off between monetary profit and fair dealing, as applied in a standard behavioral economic paradigm. I then highlight the core technical and philosophical advantages of the learning approach for modeling moral cognition, and for addressing the so-called value (...)
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  • Is It Time for Robot Rights? Moral Status in Artificial Entities.Vincent C. Müller - 2021 - Ethics and Information Technology 23 (3):579–587.
    Some authors have recently suggested that it is time to consider rights for robots. These suggestions are based on the claim that the question of robot rights should not depend on a standard set of conditions for ‘moral status’; but instead, the question is to be framed in a new way, by rejecting the is/ought distinction, making a relational turn, or assuming a methodological behaviourism. We try to clarify these suggestions and to show their highly problematic consequences. While we find (...)
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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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  • A neo-aristotelian perspective on the need for artificial moral agents.Alejo José G. Sison & Dulce M. Redín - forthcoming - AI and Society:1-19.
    We examine Van Wynsberghe and Robbins critique of the need for Artificial Moral Agents and its rebuttal by Formosa and Ryan set against a neo-Aristotelian ethical background. Neither Van Wynsberghe and Robbins essay nor Formosa and Ryan’s is explicitly framed within the teachings of a specific ethical school. The former appeals to the lack of “both empirical and intuitive support” for AMAs, and the latter opts for “argumentative breadth over depth”, meaning to provide “the essential groundwork for making an all (...)
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  • Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments.Ahmed Izzidien - 2021 - AI and Society (March 2021):1-20.
    Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing (...)
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  • Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective.Erik Hermann - forthcoming - Journal of Business Ethics:1-19.
    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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