Artificial Intelligence: the global landscape of ethics guidelines

By Anna Jobin, Marcello Ienca, Effy Vayena. 2019

2,720 citations recorded by the source corpus.

In the last five years, private companies, research institutions as well as public sector organisations have issued principles and guidelines for ethical AI, yet there is debate about both what constitutes "ethical AI" and which ethical requirements, technical standards and best practices are needed for its realization. To investigate whether a global agreement on these questions is emerging, we mapped and analyzed the current corpus of principles and guidelines on ethical AI. Our results reveal a global convergence emerging around five ethical principles (transparency, justice and fairness, non-maleficence, responsibility and privacy), with substantive divergence in relation to how these principles are interpreted; why they are deemed important; what issue, domain or actors they pertain to; and how they should be implemented. Our findings highlight the importance of integrating guideline-development efforts with substantive ethical analysis and adequate implementation strategies.

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

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Citations in the corpus

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.”
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing (2020, methodology): “We imagine this company has designated fve AI principles, paraphrased from the most commonly identifed AI principles in a current online English survey [34]–"Transparency", "Justice, Fariness & Non-Discrimination", "Safety & Non-Malefcence", "Responsibility & Accountability" and "Privacy".”
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.”
Artificial Intelligence (AI) Student Assistants in the Classroom: Designing Chatbots to Support Student Success (2022, background): “One ethical dilemma raised by Jobin et al. (2019), but not directly addressed in the 11 ethical principles identified in their literature review, is the global imbalance in the discussion of AI ethics.”
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.”
ChatGPT and a new academic reality: Artificial Intelligence‐written research papers and the ethics of the large language models in scholarly publishing (2023, background): “The ethical concerns related to the use of GPT/ ChatGPT, such as ownership of generated content and compliance with copyright laws, are applicable to AI, NLP, and chatbots as a whole (Jarrahi et al., 2022; Jobin et al., 2019).”
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).”
Fairness of artificial intelligence in healthcare: review and recommendations (2023, background): “This will ensure that AI systems can better recognize, diagnose, and treat a wide range of patient conditions, reducing disparities and promoting equity in healthcare outcomes 2.”