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    <abstract>AI systems are embedded in economic production, public discourse, governance, and personal decision-making, yet there is little empirical infrastructure for tracking whether this integration erodes humans’ ability to meaningfully shape outcomes that affect their lives. We argue that measuring AI-induced disempowerment is both urgent and tractable, and lay out a research agenda for doing so. We first operationalize disempowerment through Sen’s model of agency and a three-layer model of exposure, erosion, and lock-in, applied across economic, political, and cultural domains at individual, institutional, and civilizational scales. We survey existing measurement efforts and show that current work clusters almost entirely at exposure, leaving erosion and lock-in largely unaddressed. We then propose six concrete metrics (centaur evaluations, disempowerment perception surveys, AI content saturation and cultural convergence monitoring, monitoring capital flow to and from human labor, human task frontier tracking, and institutional ethnography) and identify which actors are best positioned to implement each. We close by discussing limitations and open challenges, including construct validity across levels of analysis, causal attribution, the distinction between disempowerment and adaptation, and the political economy of measurement.</abstract>
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%0 Conference Proceedings
%T Measuring AI-Induced Disempowerment: A Framework and Proposed Metrics
%A Chooi, Je Qin
%A Lee, Jaeho
%A Li, Jasmine Xinze
%Y Akhtar, Mubashara
%Y Batzner, Jan
%Y Choshen, Leshem
%Y Ghosh, Avijit
%Y Gohar, Usman
%Y Mickel, Jennifer
%Y Pant, Ichhya
%Y Talat, Zeerak
%Y Lin, Michelle
%S Proceedings of the Workshop on Evaluating Evaluations (EvalEval)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, CA
%@ 979-8-89176-429-3
%F chooi-etal-2026-measuring
%X AI systems are embedded in economic production, public discourse, governance, and personal decision-making, yet there is little empirical infrastructure for tracking whether this integration erodes humans’ ability to meaningfully shape outcomes that affect their lives. We argue that measuring AI-induced disempowerment is both urgent and tractable, and lay out a research agenda for doing so. We first operationalize disempowerment through Sen’s model of agency and a three-layer model of exposure, erosion, and lock-in, applied across economic, political, and cultural domains at individual, institutional, and civilizational scales. We survey existing measurement efforts and show that current work clusters almost entirely at exposure, leaving erosion and lock-in largely unaddressed. We then propose six concrete metrics (centaur evaluations, disempowerment perception surveys, AI content saturation and cultural convergence monitoring, monitoring capital flow to and from human labor, human task frontier tracking, and institutional ethnography) and identify which actors are best positioned to implement each. We close by discussing limitations and open challenges, including construct validity across levels of analysis, causal attribution, the distinction between disempowerment and adaptation, and the political economy of measurement.
%U https://aclanthology.org/2026.evaleval-1.36/
%P 227-236

Markdown (Informal)

[Measuring AI-Induced Disempowerment: A Framework and Proposed Metrics](https://aclanthology.org/2026.evaleval-1.36/) (Chooi et al., EvalEval 2026)

• Measuring AI-Induced Disempowerment: A Framework and Proposed Metrics (Chooi et al., EvalEval 2026)

ACL

• Je Qin Chooi, Jaeho Lee, and Jasmine Xinze Li. 2026. Measuring AI-Induced Disempowerment: A Framework and Proposed Metrics. In Proceedings of the Workshop on Evaluating Evaluations (EvalEval), pages 227–236, San Diego, CA. Association for Computational Linguistics.