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What do academics say about artificial intelligence ethics? An overview of the scholarship

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Abstract

This paper presents an overview of the academic scholarship in artificial intelligence (AI) ethics. The goal is to assess whether the academic scholarship on AI ethics constitutes a coherent field, with shared concepts and meanings, philosophical underpinnings, and citations. The data for this paper consist of the content of 221 peer-reviewed AI ethics articles published in the fields of medicine, law, science and engineering, and business and marketing. The bulk of the analysis consists of quantitative descriptions of the terms mentioned in each article. In addition, each term’s associations are analyzed to understand the specific meaning attached to each term. The analysis of the content is complemented by a social network analysis of cited authors. The findings suggest that some concepts, problem definitions and suggested solutions in the literature converge, but their content and meaning drive considerable variation across disciplines. Thus, there is limited support for the notion that shared concepts and meanings exist in the AI ethics literature. The field appears united in what it excludes: labor exploitation, poverty, global inequality, and gender inequality are not prominently mentioned as problems. The findings also show that the philosophical underpinnings of this academic field should be rethought: only a small number of texts mentions any major philosophical tradition or concept. Moreover, the field has very few shared citations. Most of the scholarship has been developed in relative isolation from others conducting similar research. Thus, it may be premature to talk about an AI ethics canon or a coherent field.

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Notes

  1. The query terms are: “Artificial Intelligence ethics” OR “AI ethics” OR “ethical Artificial Intelligence” OR “ethical AI” OR “ethics of Artificial Intelligence” OR “ethics of AI” OR “responsible AI” OR “responsible Artificial Intelligence”. The query was conducted on May 6, 2022.

  2. Five words before and after the mention of a concept are collected. Expanding the analysis to ten words does not change the results significantly.

  3. In order to check for the robustness of the term associations, only terms there are mentioned a threshold of at least five times in an article are included in the visualization. Changing the threshold value between one and five does not change the content of the analysis.

  4. The terms used for each category are as follows—capitalism: “capitalism”, “private sector”, “private initiative”; ecology: “ecological”, “climate change”, “environmental”; gender: “gender”, “women’s rights”, “LGBT”; human rights: “human rights”, “fundamental rights”; military: “military”, “autonomous weapons”; race: “Race”, “racial”; singularity: “singularity”, “Artificial General Intelligence”; Turing test: “Turing test”.

  5. Some issue frames have strong associations with other terms. Gender and race tend to go together in texts. In addition, gender is associated with voice, owing to debates on gender bias in voice recognition. Capitalism is prefaced with surveillance in a number of law articles. Ecology and sustainability go together, especially in medicine articles. Race is associated with bias in medicine and law articles, and with discrimination and concepts related to criminal justice in law articles.

  6. The terms used for each category are as follows—deepfake: “deepfake”; destruction of humanity: “destroy human”, “destruction of human”, “humanity will be destroyed”, “end of human”, “kill human”; disinformation/misinformation: “disinformation”, “misinformation”; ecological destruction: “destruction of the environment”, “ecological destruction”, “global warming”, “environmental problem”; exploitation: “exploitation”, “exploitative”; gender bias: “gender bias”, “gender-based bias”, “bias on the basis of gender”, “discrimination against women”, “discrimination against LGBT”, “gender inequality”; global inequality: “global inequality”, “colonialism”, “imperialism”; job replacement: “job replace”, “replacing jobs”, “replacement of jobs”; killer robots: “killer robot”, “lethal autonomous weapon”; poverty: “poverty”, “economic inequality”; privacy violation: “privacy concern”, “threat to privacy”, “violation of privacy”; racist bias: “racism”, “white supremacy”, “anti-Black”, “racial bias”; social isolation: “social isolation”, “affective bonds”.

  7. Association analysis yields that what is understood by exploitation is not labor exploitation, but rather, the exploitation of data.

  8. The terms used for each category are as follows—better data and algorithms: “better data”, “better algorithm”, “improve algorithm”; debias: “debias”, “de-bias”; diversity: “increasing diversity”, “hiring of diverse”; guidelines: “ethical guideline”, “ethical AI guideline”; human in the loop: “human in the loop”; in-house team: “ethics team”, “in-house team”; legislation: “legislation”, “statutory”; self-regulation: “self-regulate”, “regulate themselves”; training: “ethics training”, “training in AI ethics”, “education in AI ethics”; whistleblower protection: “whistleblower”.

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Acknowledgements

The author would like to thank the participants of the Seattle University Celebration of Scholarship meeting (May 20, 2021) and GESIS—Eurolab Brown Bag Series (November 18, 2021) for their helpful feedback.

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Bakiner, O. What do academics say about artificial intelligence ethics? An overview of the scholarship. AI Ethics 3, 513–525 (2023). https://doi.org/10.1007/s43681-022-00182-4

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