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AI Failure Loops in Feminized Labor: Understanding the Interplay of Workplace AI and Occupational Devaluation

Year: 2024

Type: conference-paper

Abstract: A growing body of literature has focused on understanding and addressing workplace AI design failures. However, past work has largely overlooked the role of occupational devaluation in shaping the dynamics of AI development and deployment. In this paper, we examine the case of feminized labor: a class of devalued occupations historically misnomered as ``women's work,'' such as social work, K-12 teaching, and home healthcare. Drawing on literature on AI deployments in feminized labor contexts, we conceptualize AI Failure Loops: a set of interwoven, socio-technical failures that help explain how the systemic devaluation of workers' expertise negatively impacts, and is impacted by, AI design, evaluation, and governance practices. These failures demonstrate how misjudgments on the automatability of workers' skills can lead to AI deployments that fail to bring value and, instead, further diminish the visibility of workers' expertise. We discuss research and design implications for workplace AI, especially for devalued occupations.

Source: Proceedings of the AAAI/ACM Conference on AI Ethics and Society

DOI: 10.1609/aies.v7i1.31670

Authors: Anna Kawakami, Jordan Taylor, Sarah Fox, Haiyi Zhu, Ken Holstein

Institution: Carnegie Mellon University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Gender equality, Decent work and economic growth

Open Access status: bronze

OpenAlex ID: W4404518501

FWCI: 0.485

Cites:

Cited by: 1

Related to: 10