Results for 'regulation of AI'

979 found
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  1.  38
    Towards an effective transnational regulation of AI.Daniel J. Gervais - 2023 - AI and Society 38 (1):391-410.
    Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents (AI machines) that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species (...)
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  2.  5
    Overview of AI regulation in healthcare: A comparative study of the EU and South Africa.T. Naidoo - forthcoming - South African Journal of Bioethics and Law:e2294.
    This article provides a comparative analysis of the regulatory landscapes governing artificial intelligence (AI) in healthcare in the European Union (EU) and South Africa (SA). It critically examines the approaches, frameworks and mechanisms each jurisdiction employs to balance innovation with ethical considerations, patient safety, data privacy and accountability. The EU’s proactive stance, embodied by the AI Act, offers a structured and risk-based categorisation for AI applications, emphasising stringent guidelines for risk management, data governance and human oversight. In contrast, SA’s regulatory (...)
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  3.  8
    Government regulation or industry self-regulation of AI? Investigating the relationships between uncertainty avoidance, people’s AI risk perceptions, and their regulatory preferences in Europe.Bartosz Wilczek, Sina Thäsler-Kordonouri & Maximilian Eder - forthcoming - AI and Society:1-15.
    Artificial Intelligence (AI) has the potential to influence people’s lives in various ways as it is increasingly integrated into important decision-making processes in key areas of society. While AI offers opportunities, it is also associated with risks. These risks have sparked debates about how AI should be regulated, whether through government regulation or industry self-regulation. AI-related risk perceptions can be shaped by national cultures, especially the cultural dimension of uncertainty avoidance. This raises the question of whether people in (...)
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  4.  94
    AI and the path to envelopment: knowledge as a first step towards the responsible regulation and use of AI-powered machines.Scott Robbins - 2020 - AI and Society 35 (2):391-400.
    With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be (...)
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  5.  17
    Fugazi regulation for AI: strategic tolerance for ethics washing.Gleb Papyshev & Keith Jin Deng Chan - forthcoming - AI and Society:1-12.
    Regulation theory offers a unique perspective on the institutional aspects of digital capitalism’s accumulation regime. However, a gap exists in examining the associated mode of regulation. Based on the analysis of AI ethics washing phenomenon, we suggest the state is delicately balancing between fueling innovation and reducing uncertainty in emerging technologies. This balance leads to a unique mode of regulation, "Fugazi regulation," characterized by vaguely defined, non-enforceable moral principles with no specific implementation mechanisms. We propose a (...)
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  6.  36
    The regulation of artificial intelligence.Giusella Finocchiaro - forthcoming - AI and Society:1-8.
    Before embarking on a discussion of the regulation of artificial intelligence (AI), it is first necessary to define the subject matter regulated. Defining artificial intelligence is a difficult endeavour, and many definitions have been proposed over the years. Although more than 70 years have passed since it was adopted, the most convincing definition is still nonetheless that proposed by Turing; in any case, it is important to be mindful of the risk of anthropomorphising artificial intelligence, which may arise in (...)
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  7.  37
    An exploratory qualitative analysis of AI ethics guidelines.Aline Shakti Franzke - 2022 - Journal of Information, Communication and Ethics in Society 20 (4):401-423.
    Purpose As Big Data and Artificial Intelligence (AI) proliferate, calls have emerged for ethical reflection. Ethics guidelines have played a central role in this respect. While quantitative research on the ethics guidelines of AI/Big Data has been undertaken, there has been a dearth of systematic qualitative analyses of these documents. Design/methodology/approach Aiming to address this research gap, this paper analyses 70 international ethics guidelines documents from academia, NGOs and the corporate realm, published between 2017 and 2020. Findings The article presents (...)
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  8.  12
    Regulating generative AIs: (Re)defining video games as cultural products.Manh-Toan Ho - forthcoming - AI and Society:1-2.
  9.  37
    Examining embedded apparatuses of AI in Facebook and TikTok.Justin Grandinetti - forthcoming - AI and Society:1-14.
    In popular discussions, the nuances of AI are often abridged as “the algorithm”, as the specific arrangements of machine learning, deep learning and automated decision-making on social media platforms are typically shrouded in proprietary secrecy punctuated by press releases and transparency initiatives. What is clear, however, is that AI embedded on social media functions to recommend content, personalize ads, aggregate news stories, and moderate problematic material. It is also increasingly apparent that individuals are concerned with the uses, implications, and fairness (...)
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  10.  12
    Value-laden challenges for technical standards supporting regulation in the field of AI.Alessio Tartaro - 2024 - Ethics and Information Technology 26 (4):1-12.
    This perspective paper critically examines value-laden challenges that emerge when using standards to support regulation in the field of artificial intelligence, particularly within the context of the AI Act. It presents a dilemma arising from the inherent vagueness and contestable nature of the AI Act’s requirements. The effective implementation of these requirements necessitates addressing hard normative questions that involve complex value judgments. These questions, such as determining the acceptability of risks or the appropriateness of accuracy levels, need to be (...)
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  11. Generative AI and the Future of Democratic Citizenship.Paul Formosa, Bhanuraj Kashyap & Siavosh Sahebi - 2024 - Digital Government: Research and Practice 2691 (2024/05-ART).
    Generative AI technologies have the potential to be socially and politically transformative. In this paper, we focus on exploring the potential impacts that Generative AI could have on the functioning of our democracies and the nature of citizenship. We do so by drawing on accounts of deliberative democracy and the deliberative virtues associated with it, as well as the reciprocal impacts that social media and Generative AI will have on each other and the broader information landscape. Drawing on this background (...)
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  12.  47
    The Right Not to Be Subjected to AI Profiling Based on Publicly Available Data—Privacy and the Exceptionalism of AI Profiling.Thomas Ploug - 2023 - Philosophy and Technology 36 (1):1-22.
    Social media data hold considerable potential for predicting health-related conditions. Recent studies suggest that machine-learning models may accurately predict depression and other mental health-related conditions based on Instagram photos and Tweets. In this article, it is argued that individuals should have a sui generis right not to be subjected to AI profiling based on publicly available data without their explicit informed consent. The article (1) develops three basic arguments for a right to protection of personal data trading on the notions (...)
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  13.  27
    AI as a boss? A national US survey of predispositions governing comfort with expanded AI roles in society.Kate K. Mays, Yiming Lei, Rebecca Giovanetti & James E. Katz - 2022 - AI and Society 37 (4):1587-1600.
    People’s comfort with and acceptability of artificial intelligence (AI) instantiations is a topic that has received little systematic study. This is surprising given the topic’s relevance to the design, deployment and even regulation of AI systems. To help fill in our knowledge base, we conducted mixed-methods analysis based on a survey of a representative sample of the US population (_N_ = 2254). Results show that there are two distinct social dimensions to comfort with AI: as a peer and as (...)
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  14. (1 other version)Ethics as a service: a pragmatic operationalisation of AI ethics.Jessica Morley, Anat Elhalal, Francesca Garcia, Libby Kinsey, Jakob Mökander & Luciano Floridi - 2021 - Minds and Machines 31 (2):239–256.
    As the range of potential uses for Artificial Intelligence, in particular machine learning, has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory (...)
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  15.  13
    Scoring the Ethics of AI Robo-Advice: Why We Need Gateways and Ratings.Paul Kofman - forthcoming - Journal of Business Ethics:1-13.
    Unlike the many services already transformed by artificial intelligence (_AI_), the financial advice sector remains committed to a human interface. That is surprising as an AI-powered financial advisor (a _robo-advisor_) can offer personalised financial advice at much lower cost than traditional human advice. This is particularly important for those who need but cannot afford or access traditional financial advice. Robo-advice is easily accessible, available on-demand, and pools all relevant information in finding and implementing an optimal financial plan. In a perfectly (...)
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  16.  53
    Pauses, parrots, and poor arguments: real-world constraints undermine recent calls for AI regulation.Bartek Chomanski - 2023 - AI and Society.
    Many leading intellectuals, technologists, commentators, and ordinary people have in recent weeks become embroiled in a fiery debate (yet to hit the pages of scholarly journals) on the alleged need to press pause on the development of generative artificial intelligence (AI). Spurred by an open letter from the Future of Life Institute (FLI) calling for just such a pause, the debate occasioned, at lightning speed, a large number of responses from a variety of sources pursuing a variety of argumentative strategies. (...)
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  17. Decentralized Governance of AI Agents.Tomer Jordi Chaffer, Charles von Goins Ii, Bayo Okusanya, Dontrail Cotlage & Justin Goldston - manuscript
    Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI (...)
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  18.  81
    The Oxford Handbook of AI Governance.Justin B. Bullock, Yu-Che Chen, Johannes Himmelreich, Valerie M. Hudson, Anton Korinek, Matthew M. Young & Baobao Zhang (eds.) - 2023 - Oxford University Press.
    As the capabilities of Artificial Intelligence (AI) have increased over recent years, so have the challenges of how to govern its usage. Consequently, prominent stakeholders across academia, government, industry, and civil society have called for states to devise and deploy principles, innovative policies, and best practices to regulate and oversee these increasingly powerful AI tools. Developing a robust AI governance system requires extensive collective efforts throughout the world. It also raises old questions of politics, democracy, and administration, but with the (...)
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  19. Oxford Handbook on the Foundations and Regulation of Generative AI.Philipp Hacker, Brent Mittelstadt, Sarah Hammer & Andreas Engel (eds.) - forthcoming
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  20.  22
    Regulating AI in Health Care: The Challenges of Informed User Engagement.Olya Kudina - 2021 - Hastings Center Report 51 (5):6-7.
    Hastings Center Report, Volume 51, Issue 5, Page 6-7, September‐October 2021.
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  21.  12
    Regulating AI-Based Medical Devices in Saudi Arabia: New Legal Paradigms in an Evolving Global Legal Order.Barry Solaiman - 2024 - Asian Bioethics Review 16 (3):373-389.
    This paper examines the Saudi Food and Drug Authority’s (SFDA) Guidance on Artificial Intelligence (AI) and Machine Learning (ML) technologies based Medical Devices (the MDS-G010). The SFDA has pioneered binding requirements designed for manufacturers to obtain Medical Device Marketing Authorization. The regulation of AI in health is at an early stage worldwide. Therefore, it is critical to examine the scope and nature of the MDS-G010, its influences, and its future directions. It is argued that the guidance is a patchwork (...)
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  22.  41
    Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study.Hisham E. Hasan, Deema Jaber, Omar F. Khabour & Karem H. Alzoubi - 2024 - BMC Medical Ethics 25 (1):1-11.
    Background Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges. Methods A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests. Results Participants expressed concerns (...)
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  23.  49
    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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  24. The emperor is naked: Moral diplomacies and the ethics of AI.Constantin Vica, Cristina Voinea & Radu Uszkai - 2021 - Információs Társadalom 21 (2):83-96.
    With AI permeating our lives, there is widespread concern regarding the proper framework needed to morally assess and regulate it. This has given rise to many attempts to devise ethical guidelines that infuse guidance for both AI development and deployment. Our main concern is that, instead of a genuine ethical interest for AI, we are witnessing moral diplomacies resulting in moral bureaucracies battling for moral supremacy and political domination. After providing a short overview of what we term ‘ethics washing’ in (...)
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  25.  46
    An Institutionalist Approach to AI Ethics: Justifying the Priority of Government Regulation over Self-Regulation.Thomas Ferretti - 2022 - Moral Philosophy and Politics 9 (2):239-265.
    This article explores the cooperation of government and the private sector to tackle the ethical dimension of artificial intelligence. The argument draws on the institutionalist approach in philosophy and business ethics defending a ‘division of moral labor’ between governments and the private sector. The goal and main contribution of this article is to explain how this approach can provide ethical guidelines to the AI industry and to highlight the limits of self-regulation. In what follows, I discuss three institutionalist claims. (...)
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  26.  90
    AI in the headlines: the portrayal of the ethical issues of artificial intelligence in the media.Leila Ouchchy, Allen Coin & Veljko Dubljević - 2020 - AI and Society 35 (4):927-936.
    As artificial intelligence technologies become increasingly prominent in our daily lives, media coverage of the ethical considerations of these technologies has followed suit. Since previous research has shown that media coverage can drive public discourse about novel technologies, studying how the ethical issues of AI are portrayed in the media may lead to greater insight into the potential ramifications of this public discourse, particularly with regard to development and regulation of AI. This paper expands upon previous research by systematically (...)
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  27.  9
    Normative Challenges of Risk Regulation of Artificial Intelligence.Carsten Orwat, Jascha Bareis, Anja Folberth, Jutta Jahnel & Christian Wadephul - 2024 - NanoEthics 18 (2):1-29.
    Approaches aimed at regulating artificial intelligence (AI) include a particular form of risk regulation, i.e. a risk-based approach. The most prominent example is the European Union’s Artificial Intelligence Act (AI Act). This article addresses the challenges for adequate risk regulation that arise primarily from the specific type of risks involved, i.e. risks to the protection of fundamental rights and fundamental societal values. This is mainly due to the normative ambiguity of such rights and societal values when attempts are (...)
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  28.  46
    The Dawn of the AI Robots: Towards a New Framework of AI Robot Accountability.Zsófia Tóth, Robert Caruana, Thorsten Gruber & Claudia Loebbecke - 2022 - Journal of Business Ethics 178 (4):895-916.
    Business, management, and business ethics literature pay little attention to the topic of AI robots. The broad spectrum of potential ethical issues pertains to using driverless cars, AI robots in care homes, and in the military, such as Lethal Autonomous Weapon Systems. However, there is a scarcity of in-depth theoretical, methodological, or empirical studies that address these ethical issues, for instance, the impact of morality and where accountability resides in AI robots’ use. To address this dearth, this study offers a (...)
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  29.  59
    Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation.Jakob Mökander, Maria Axente, Federico Casolari & Luciano Floridi - 2022 - Minds and Machines 32 (2):241-268.
    The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the _conformity assessments_ that providers of high-risk AI systems are expected to conduct, and (...)
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  30.  45
    Companies Committed to Responsible AI: From Principles towards Implementation and Regulation?Paul B. de Laat - 2021 - Philosophy and Technology 34 (4):1135-1193.
    The term ‘responsible AI’ has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the ‘Partnership on AI’. By means of a comprehensive web search, two questions are addressed by this study: (...)
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  31.  44
    The selective deployment of AI in healthcare.Robert Vandersluis & Julian Savulescu - 2024 - Bioethics 38 (5):391-400.
    Machine‐learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well‐represented populations. Faced with this dilemma (...)
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  32.  12
    Patient Consent and The Right to Notice and Explanation of AI Systems Used in Health Care.Meghan E. Hurley, Benjamin H. Lang, Kristin Marie Kostick-Quenet, Jared N. Smith & Jennifer Blumenthal-Barby - 2024 - American Journal of Bioethics 25 (3):102-114.
    Given the need for enforceable guardrails for artificial intelligence (AI) that protect the public and allow for innovation, the U.S. Government recently issued a Blueprint for an AI Bill of Rights which outlines five principles of safe AI design, use, and implementation. One in particular, the right to notice and explanation, requires accurately informing the public about the use of AI that impacts them in ways that are easy to understand. Yet, in the healthcare setting, it is unclear what goal (...)
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  33.  33
    Digitalisation and the regulation of work: theoretical issues and normative challenges.Angelo Salento - 2018 - AI and Society 33 (3):369-378.
    This paper presents an introductory overview of the main issues that the digitalisation of industrial enterprises known as Industry 4.0 raises for social sciences. First, it will show that this technological transition—which, however, is unfinished and is seen to be in continuity with the so-called “third industrial revolution”—cannot be interpreted with reference to a deterministic approach. It can be analysed more usefully as a range of decisions affecting the industrial policies of national states, the conception and design of machines, their (...)
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  34. Somatic genome editing with the use of AI : big promises but doubled legal issues.Anastasiya Kiseleva - 2023 - In Santa Slokenberga, Timo Minssen & Ana Nordberg, Governing, protecting, and regulating the future of genome editing: the significance of ELSPI perspectives. Boston: Brill/Nijhoff.
     
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  35. Balancing risks and benefits: public perceptions of AI through traditional surveys and social media analysis.Daniel Kouloukoui, Nathalie de Marcellis-Warin & Thierry Warin - forthcoming - AI and Society:1-24.
    The rapid evolution of Artificial Intelligence (AI) has broadened discussions about its social and technological implications. The purpose of this study is to examine the perception of risks and benefits associated with AI, focusing on privacy, cybersecurity, trust in government management, and online device acceptance over time. We used an integrative approach that combined questionnaires and data analysis from X via Natural Language Processing (NLP). The traditional survey had 1013 participants in 2018 and 1000 in 2021, while X’s analysis examined (...)
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  36. Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque.Uwe Peters - forthcoming - AI and Ethics.
    Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...)
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  37.  21
    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 (...)
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  38.  8
    The state of cultural biology: regulating biological computing.James Griffin - 2023 - Cheltenham, UK: Edward Elgar Publishing.
    Offering a novel and pragmatic perspective, this timely book critically examines the development of a culture of machinist regulation and questions whether this approach is appropriate in an era of rising biological technologies. Adopting an ontological approach, James Griffin considers how current regulatory frameworks favour digital technology and how this may change in the future. Griffin adeptly investigates how regulation can impact the nature of new technologies, especially as biological computing is becoming more commonplace. Chapters provide a wealth (...)
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  39. Varieties of transparency: exploring agency within AI systems.Gloria Andrada, Robert William Clowes & Paul Smart - 2023 - AI and Society 38 (4):1321-1331.
    AI systems play an increasingly important role in shaping and regulating the lives of millions of human beings across the world. Calls for greater _transparency_ from such systems have been widespread. However, there is considerable ambiguity concerning what “transparency” actually means, and therefore, what greater transparency might entail. While, according to some debates, transparency requires _seeing through_ the artefact or device, widespread calls for transparency imply _seeing into_ different aspects of AI systems. These two notions are in apparent tension with (...)
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  40.  1
    DECL AI NE: How the Design of AI is Eroding our Humanity.Eve Poole - forthcoming - Studies in Christian Ethics.
    In the race to develop Artificial General Intelligence, an assumption within the AI community is that an artificial brain that complex would naturally develop consciousness. This spectre has produced calls for a global pause while regulation catches up, because of fears about ‘the Control Problem’, or what happens if AI goes rogue. This article will look at how this problem is tackled in human design. It seems that most of the elements of our design that are salient for the (...)
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  41.  23
    Innovation, risk and control: The true trend is ‘from tool to purpose’—A discussion on the standardization of AI.Oriana Chaves - forthcoming - AI and Society:1-12.
    In this text, our question is what is the current regulatory trend in countries that are not considered central in the development of artificial intelligence, such as Brazil: a preventive approach, or an experimental approach? We will analyze the bills (PL) that are being processed in legislative houses at the state level, and at the federal level, highlighting some elements, such as: Delimitation of the object (conceptualization), fundamental principles, ethical guidelines, relationship with human work, human supervision, and guidelines for public (...)
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  42.  50
    Ethics of using artificial intelligence (AI) in veterinary medicine.Simon Coghlan & Thomas Quinn - 2023 - AI and Society (5):2337-2348.
    This paper provides the first comprehensive analysis of ethical issues raised by artificial intelligence (AI) in veterinary medicine for companion animals. Veterinary medicine is a socially valued service, which, like human medicine, will likely be significantly affected by AI. Veterinary AI raises some unique ethical issues because of the nature of the client–patient–practitioner relationship, society’s relatively minimal valuation and protection of nonhuman animals and differences in opinion about responsibilities to animal patients and human clients. The paper examines how these distinctive (...)
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  43.  20
    Privacy Considerations in the Canadian Regulation of Commercially-Operated Healthcare Artificial Intelligence.Blake Murdoch, Allison Jandura & Timothy Caulfield - 2022 - Canadian Journal of Bioethics / Revue canadienne de bioéthique 5 (4):44-52.
    Artificial intelligence (AI) is increasingly being developed and implemented in healthcare. This presents privacy issues since many AIs are privately owned and rely on data sharing arrangements for mass quantities of patient health information. We investigated the Canadian legal and policy framework focusing on regulation relevant to the potential for inappropriate use or disclosure of personal health information by private AI companies. This included analysis of federal and provincial legislation, common law and research ethics policy. Our evaluation of the (...)
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  44.  11
    AI and the iterable epistopics of risk.Andy Crabtree, Glenn McGarry & Lachlan Urquhart - forthcoming - AI and Society:1-14.
    The risks AI presents to society are broadly understood to be manageable through ‘general calculus’, i.e., general frameworks designed to enable those involved in the development of AI to apprehend and manage risk, such as AI impact assessments, ethical frameworks, emerging international standards, and regulations. This paper elaborates how risk is apprehended and managed by a regulator, developer and cyber-security expert. It reveals that risk and risk management is dependent on mundane situated practices not encapsulated in general calculus. Situated practice (...)
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  45.  28
    The right to a second opinion on Artificial Intelligence diagnosis—Remedying the inadequacy of a risk‐based regulation.Thomas Ploug & Søren Holm - 2022 - Bioethics 37 (3):303-311.
    In this paper, we argue that patients who are subjects of Artificial Intelligence (AI)-supported diagnosis and treatment planning should have a right to a second opinion, but also that this right should not necessarily be construed as a right to a physician opinion. The right to a second opinion could potentially be satisfied by another independent AI system. Our considerations on the right to second opinion are embedded in the wider debate on different approaches to the regulation of AI, (...)
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  46.  38
    AI models and the future of genomic research and medicine: True sons of knowledge?Harald König, Daniel Frank, Martina Baumann & Reinhard Heil - 2021 - Bioessays 43 (10):2100025.
    The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if (...)
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  47.  51
    AI Case Studies: Potential for Human Health, Space Exploration and Colonisation and a Proposed Superimposition of the Kubler-Ross Change Curve on the Hype Cycle.Martin Braddock & Matthew Williams - 2019 - Studia Humana 8 (1):3-18.
    The development and deployment of artificial intelligence (AI) is and will profoundly reshape human society, the culture and the composition of civilisations which make up human kind. All technological triggers tend to drive a hype curve which over time is realised by an output which is often unexpected, taking both pessimistic and optimistic perspectives and actions of drivers, contributors and enablers on a journey where the ultimate destination may be unclear. In this paper we hypothesise that this journey is not (...)
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  48. Balancing AI and academic integrity: what are the positions of academic publishers and universities?Bashar Haruna Gulumbe, Shuaibu Muhammad Audu & Abubakar Muhammad Hashim - forthcoming - AI and Society:1-10.
    This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI in (...)
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  49.  35
    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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  50. 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 (...)
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