The global landscape of AI ethics guidelines

By Anna Jobin, Marcello Ienca, Effy Vayena. 2019

2,263 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Public Health, Environmental and Occupational Health, Safety Research, Safety, Robustness & Interpretability

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Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “The call for a global lens in AI accountability is not new [84, 155], but the ethical principles in AI are often interpreted, prioritised, contextualised, and implemented differently across the globe [98].”
Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “Jobin et al. (2019) reviewed 84 documents, which were produced by several actors of the ﬁ eld, almost half of which from private companies or governmental agencies.”
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, background): “Further, they are amenable to various interpretations [13][14] and agreement on them can mask deeper disagreements [5].”
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence (2020, background): “A review of the global landscape of AI ethics guidelines (Jobin et al., 2019) pointed out the under-representation of geographic areas such as Africa, South and Central America and Central Asia in the AI ethics debate.”
Trustworthy AI: From Principles to Practices (2021, background): “Comprehensive statistics relating to and comparisons between these principles have been provided in References [145, 178].”
Design Principles for Generative AI Applications (2024, background): “…guidelines focus on specifc types of AI technologies (e.g. text-to-image models [104]), specifc domains of use (e.g. creative writing [24]), or specifc issues regarding the use of AI, including ethics [11, 59, 69, 72], fairness [110], human rights [50], explainabil-ity [119], and user trust [186].”
Principles alone cannot guarantee ethical AI (2019, neutral): “nt suggest a principled approach may have limited impact on design and governance. Compared to medicine, AI development lacks (1) common aims and fiduciary duties, (2) professional history and norms, (3) proven methods to translate principles into practice, and (4) robust legal and professional acco”
Towards intellectual freedom in an AI Ethics Global Community (2021, background): “Problems of systemic racism and other culturally deeply encoded discriminatory mechanisms multiply the severity of such conflicts of interest insignificant, perhaps non-linear and even counter-intuitive ways [22, 24, 36, 38, 39, 44].”
Transparency in artificial intelligence (2020, background): “In fact, “transparency” is the single most common, and one of the key five principles emphasised in the vast number – a recent study counted 84 – of ethical guidelines addressing AI on a global level (Jobin et al., 2019).”
Critical Perspectives on Governance Mechanisms for AI/ML Systems (2020, background): “Jobin, Ienca, and Vayena (2019) observe some high-level principles such as trans-parency are commonly invoked across many of these statements, while others, like sustainability and solidarity, are far less common.”
Trustworthy artificial intelligence (2020, background): “There are, for example, currently more than 60 high-level guidelines for the development and deployment of ethical AI (Jobin et al. 2019).”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “Interested readers are referred to several comprehensive data ethics resources [225], [231]–[235], to a recent report from the TUM Institute for Ethics in Artificial Intelligence [226], and the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems [227] on the ethical challenges inv”
Exploring AI Ethics of ChatGPT: A Diagnostic Analysis (2023, background): “The objective of our study is not to provide a flawless, quantitative, and deterministic solution for designing a responsible language model application, echoing the history of scientific advancement[30, 31].”
Trust in AI: progress, challenges, and future directions (2024, positive): “Although transparency and explainability have been usually categorized under the same ethical principle (Jobin et al., 2019a), it is essential to distinguish between these two different topics before extensive interchangeable misuse of them.”
Auditing large language models: a three-layered approach (2023, background): “, the adherence to the laws, regulations, guidelines, and specifications relevant to a specific application [220], as well as to voluntary ethics principles [221] or codes of conduct [222].”
Conversational AI: Social and Ethical Considerations (2019, background): “…major companies, research institutions, and public sector organizations have all issued guidelines for ethical artiﬁcial intelligence, recent work [22] has discovered substantive divergence in how these are written and interpreted, highlighting the complexity of designing guidelines for systems…”