Epistemic Power in AI Ethics Labor: Legitimizing Located Complaints
ACM Conference on Fairness, Accountability and Transparency(2024)
摘要
What counts as legitimate AI ethics labor, and consequently, what are the
epistemic terms on which AI ethics claims are rendered legitimate? Based on 75
interviews with technologists including researchers, developers, open source
contributors, and activists, this paper explores the various epistemic bases
from which AI ethics is discussed and practiced. In the context of outside
attacks on AI ethics as an impediment to "progress," I show how some AI ethics
practices have reached toward authority from automation and quantification, and
achieved some legitimacy as a result, while those based on richly embodied and
situated lived experience have not. This paper draws together the work of
feminist Anthropology and Science and Technology Studies scholars Diana
Forsythe and Lucy Suchman with the works of postcolonial feminist theorist Sara
Ahmed and Black feminist theorist Kristie Dotson to examine the implications of
dominant AI ethics practices.
By entrenching the epistemic power of quantification, dominant AI ethics
practices – employing Model Cards and similar interventions – risk
legitimizing AI ethics as a project in equal and opposite measure to which they
marginalize embodied lived experience as a legitimate part of the same project.
In response, I propose humble technical practices: quantified or technical
practices which specifically seek to make their epistemic limits clear in order
to flatten hierarchies of epistemic power.
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