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  1. More Process, Less Principles: The Ethics of Deploying AI and Robotics in Medicine.Amitabha Palmer & David Schwan - 2024 - Cambridge Quarterly of Healthcare Ethics 33 (1):121-134.
    Current national and international guidelines for the ethical design and development of artificial intelligence (AI) and robotics emphasize ethical theory. Various governing and advisory bodies have generated sets of broad ethical principles, which institutional decisionmakers are encouraged to apply to particular practical decisions. Although much of this literature examines the ethics of designing and developing AI and robotics, medical institutions typically must make purchase and deployment decisions about technologies that have already been designed and developed. The primary problem facing medical (...)
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  • Policing based on automatic facial recognition.Zhilong Guo & Lewis Kennedy - 2023 - Artificial Intelligence and Law 31 (2):397-443.
    Advances in technology have transformed and expanded the ways in which policing is run. One new manifestation is the mass acquisition and processing of private facial images via automatic facial recognition by the police: what we conceptualise as AFR-based policing. However, there is still a lack of clarity on the manner and extent to which this largely-unregulated technology is used by law enforcement agencies and on its impact on fundamental rights. Social understanding and involvement are still insufficient in the context (...)
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  • AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law.Floris J. Bex - forthcoming - Artificial Intelligence and Law:1-18.
    We live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will demonstrate (...)
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  • Track Thyself? The Value and Ethics of Self-knowledge Through Technology.Muriel Leuenberger - 2024 - Philosophy and Technology 37 (1):1-22.
    Novel technological devices, applications, and algorithms can provide us with a vast amount of personal information about ourselves. Given that we have ethical and practical reasons to pursue self-knowledge, should we use technology to increase our self-knowledge? And which ethical issues arise from the pursuit of technologically sourced self-knowledge? In this paper, I explore these questions in relation to bioinformation technologies (health and activity trackers, DTC genetic testing, and DTC neurotechnologies) and algorithmic profiling used for recommender systems, targeted advertising, and (...)
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  • Artificial intelligence-related anomies and predictive policing: normative (dis)orders in liberal democracies.Klaus Behnam Shad - forthcoming - AI and Society:1-12.
    This article links three rarely considered dimensions related to the implementation of artificial intelligence (AI)-based technologies in the form of predictive policing and discusses them in relation to liberal democratic societies. The three dimensions are the theoretical embedding and the workings of AI within anomic conditions (1), potential normative disorders emerging from them in the form of thinking errors and discriminatory practices (2) as well as the consequences of these disorders on the psychosocial, and emotional level (3). Against this background, (...)
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  • Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning.Ryan Jenkins, Kristian Hammond, Sarah Spurlock & Leilani Gilpin - forthcoming - AI and Society:1-14.
    In this paper, we outline a new method for evaluating the human impact of machine-learning applications. In partnership with Underwriters Laboratories Inc., we have developed a framework to evaluate the impacts of a particular use of machine learning that is based on the goals and values of the domain in which that application is deployed. By examining the use of artificial intelligence in particular domains, such as journalism, criminal justice, or law, we can develop more nuanced and practically relevant understandings (...)
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