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Hidden Agents, Explicit Obligations: A Linguistic Analysis of AI Ethics Guidelines.

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Title: Hidden Agents, Explicit Obligations: A Linguistic Analysis of AI Ethics Guidelines.
Authors: Griffin, Tricia A.1,2 (AUTHOR) griffin.triciaann@gmail.com, Goorman, Roos1 (AUTHOR) regrmn@outlook.com, Green, Brian P.3 (AUTHOR) bpgreen@scu.edu, Welie, Jos V. M.1,4 (AUTHOR) j.welie@maastrichtuniversity.nl
Source: Science & Engineering Ethics. Dec2025, Vol. 31 Issue 6, p1-22. 22p.
Subjects: Moral agent (Philosophy), Computer software developers, Ethics, Linguistics, Transitivity (Grammar), Artificial intelligence & ethics, Normativity (Ethics), Change agents, Standards, Communication ethics
Abstract: Since 2013, many organizations, governments, and coalitions have issued ethics guidelines aimed at achieving ethically sound artificial intelligence (AI). The literature evaluating these guidelines has so far focused more on what is in them (e.g., principles) than on who is expected to enact them (e.g., developers). We argue that ethical agency in AI Ethics guidelines has been under-scrutinized in the literature, and we seek here to fill that gap. This study relies on transitivity analysis to evaluate 87 operational ethics guidelines for the representation of moral agents and their agency. We identified normative key words, their linguistic function, and agency attribution in 6,935 statements. Our findings reveal 11 distinct agents, with deployers, developers, and AI systems being the most frequently invoked. However, the ethical agency attributed to developers and deployers was overwhelmingly implied, while the tasks assigned to them were more often normative than descriptive. That the agency of the two most powerful agents in AI development is so often hidden in ethics guidelines reveals that the challenges associated with implementing AI ethics guidelines does not stem merely from the "principles to practice" problem, but from a more deeply rooted issue regarding how guideline authors conceive of ethical agency. We evaluate the findings through the dual lenses of agent backgrounding and agent suppression and conclude with advice for authors of ethics guidelines. [ABSTRACT FROM AUTHOR]
Copyright of Science & Engineering Ethics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Since 2013, many organizations, governments, and coalitions have issued ethics guidelines aimed at achieving ethically sound artificial intelligence (AI). The literature evaluating these guidelines has so far focused more on what is in them (e.g., principles) than on who is expected to enact them (e.g., developers). We argue that ethical agency in AI Ethics guidelines has been under-scrutinized in the literature, and we seek here to fill that gap. This study relies on transitivity analysis to evaluate 87 operational ethics guidelines for the representation of moral agents and their agency. We identified normative key words, their linguistic function, and agency attribution in 6,935 statements. Our findings reveal 11 distinct agents, with deployers, developers, and AI systems being the most frequently invoked. However, the ethical agency attributed to developers and deployers was overwhelmingly implied, while the tasks assigned to them were more often normative than descriptive. That the agency of the two most powerful agents in AI development is so often hidden in ethics guidelines reveals that the challenges associated with implementing AI ethics guidelines does not stem merely from the "principles to practice" problem, but from a more deeply rooted issue regarding how guideline authors conceive of ethical agency. We evaluate the findings through the dual lenses of agent backgrounding and agent suppression and conclude with advice for authors of ethics guidelines. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Science & Engineering Ethics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Dec2025
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