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  1. Artificial Aesthetics and Ethical Ambiguity: Exploring Business Ethics in the Context of AI-driven Creativity.Cheng Xu, Yanqi Sun & Haibo Zhou - forthcoming - Journal of Business Ethics:1-22.
    In an era of technological ubiquity, artificial intelligence (AI) is reshaping not only industries but also fundamental human experiences, including artistic creativity. Rooted in a Posthumanist theoretical framework, this research scrutinizes the intricate ethical and aesthetic challenges that artists confront in AI-enabled art creation, with a particular focus on a novel phenomenon we term 'aesthetic loss of control.’ This phenomenon bears significant implications for notions of authorship, copyright, and business ethics in the art industry. Utilizing a mixed-methods approach, our study (...)
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  • Artificial intelligence and conversational agent evolution – a cautionary tale of the benefits and pitfalls of advanced technology in education, academic research, and practice.Curtis C. Cain, Carlos D. Buskey & Gloria J. Washington - 2023 - Journal of Information, Communication and Ethics in Society 21 (4):394-405.
    Purpose The purpose of this paper is to demonstrate the advancements in artificial intelligence (AI) and conversational agents, emphasizing their potential benefits while also highlighting the need for vigilant monitoring to prevent unethical applications. Design/methodology/approach As AI becomes more prevalent in academia and research, it is crucial to explore ways to ensure ethical usage of the technology and to identify potentially unethical usage. This manuscript uses a popular AI chatbot to write the introduction and parts of the body of a (...)
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  • Creating meaningful work in the age of AI: explainable AI, explainability, and why it matters to organizational designers.Kristin Wulff & Hanne Finnestrand - forthcoming - AI and Society:1-14.
    In this paper, we contribute to research on enterprise artificial intelligence (AI), specifically to organizations improving the customer experiences and their internal processes through using the type of AI called machine learning (ML). Many organizations are struggling to get enough value from their AI efforts, and part of this is related to the area of explainability. The need for explainability is especially high in what is called black-box ML models, where decisions are made without anyone understanding how an AI reached (...)
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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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  • Moral Awareness of College Students Regarding Artificial Intelligence.Manh Tung Ho & Nader Ghotbi - 2021 - Asian Bioethics Review 13 (4):421-433.
    To evaluate the moral awareness of college students regarding artificial intelligence (AI) systems, we have examined 467 surveys collected from 152 Japanese and 315 non-Japanese students in an international university in Japan. The students were asked to choose a most significant moral problem of AI applications in the future from a list of ten ethical issues and to write an essay about it. The results show that most of the students (n = 269, 58%) considered unemployment to be the major (...)
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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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  • A phenomenological perspective on AI ethical failures: The case of facial recognition technology.Yuni Wen & Matthias Holweg - forthcoming - AI and Society:1-18.
    As more and more companies adopt artificial intelligence to increase the efficiency and effectiveness of their products and services, they expose themselves to ethical crises and potentially damaging public controversy associated with its use. Despite the prevalence of AI ethical problems, most companies are strategically unprepared to respond effectively to the public. This paper aims to advance our empirical understanding of company responses to AI ethical crises by focusing on the rise and fall of facial recognition technology. Specifically, through a (...)
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  • Operationalising AI ethics: how are companies bridging the gap between practice and principles? An exploratory study.Javier Camacho Ibáñez & Mónica Villas Olmeda - 2022 - AI and Society 37 (4):1663-1687.
    Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and two focus groups, (...)
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  • An AI ethics ‘David and Goliath’: value conflicts between large tech companies and their employees.Mark Ryan, Eleni Christodoulou, Josephina Antoniou & Kalypso Iordanou - forthcoming - AI and Society:1-16.
    Artificial intelligence ethics requires a united approach from policymakers, AI companies, and individuals, in the development, deployment, and use of these technologies. However, sometimes discussions can become fragmented because of the different levels of governance or because of different values, stakeholders, and actors involved. Recently, these conflicts became very visible, with such examples as the dismissal of AI ethics researcher Dr. Timnit Gebru from Google and the resignation of whistle-blower Frances Haugen from Facebook. Underpinning each debacle was a conflict between (...)
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  • On the Contribution of Neuroethics to the Ethics and Regulation of Artificial Intelligence.Michele Farisco, Kathinka Evers & Arleen Salles - 2022 - Neuroethics 15 (1):1-12.
    Contemporary ethical analysis of Artificial Intelligence is growing rapidly. One of its most recognizable outcomes is the publication of a number of ethics guidelines that, intended to guide governmental policy, address issues raised by AI design, development, and implementation and generally present a set of recommendations. Here we propose two things: first, regarding content, since some of the applied issues raised by AI are related to fundamental questions about topics like intelligence, consciousness, and the ontological and ethical status of humans, (...)
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