Measuring AI-Induced Disempowerment: A Framework and Proposed Metrics

Je Qin Chooi, Jasmine Xinze Li, Jaeho Lee

Keywords: disempowerment, measurement, AI

TL;DR: We develop a framework to measure human disempowerment due to AI and propose six concrete metrics to track disempowerment.

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.

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Submission Type: Provocation

Archival Status: Non-archival

Submission Number: 80