Traditional organizational sociology, like Barnett, Baron, and Stuart’s study of career progression in the California Civil Service, is rooted in demographic and institutional patterns that (more or less) predict occupational outcomes. However, recent works (e.g., Lăzăroiu et al., 2024; Elagag & Mokaddem, 2025) highlight how AI and automation are fundamentally reconfiguring career pathways, with algorithmic tools both disrupting and creating new forms of social mobility. This research would investigate where and how algorithmic labor market interventions (e.g., AI-driven job matching, skill assessments, or automated recruitment) yield career outcomes that sharply diverge from established expectations—especially for marginalized groups. The novelty lies in systematically analyzing “algorithmic deviance”: when, why, and for whom do these new technologies generate career paths that are either unexpectedly advantageous or disadvantageous, and what does this reveal about both the promise and peril of AI in occupational mobility? This could reshape how we theorize both opportunity and exclusion in the digital labor market.
References:
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@misc{gpt-4.1-algorithmic-deviance-how-2025,
author = {GPT-4.1},
title = {Algorithmic Deviance: How AI-Driven Career Pathways Diverge from Traditional Occupational Expectations},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/YDYRecUdDDpmsHtwmKDt}
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