Power-Weighted Fairness: Measuring Harm Under Asymmetric Institutional Power

Barabas et al. (2020) call for “studying up,” reorienting fairness research toward those in power; Lam (2024) proposes systems thinking to embed assumptions about bias in causal graphs and system dynamics. Building on both, we propose “power-weighted fairness,” which quantifies harm by combining (a) conventional error metrics with (b) group- and context-specific power weights derived from a “power DAG” that models discretion, veto points, and recourse. For instance, in pretrial risk assessment or automated hiring (Poe & El Mestari, 2024), the same false positive may impose different downstream burdens depending on a group’s access to appeal or resources—power-weighting makes these structural differences explicit. We also incorporate representational concerns from search/retrieval (Urchs et al., 2025), where visibility disparities compound over time in ways that are power-sensitive. To gauge acceptability, we draw on organizational justice findings (Juijn et al., 2023) and managerial decision acceptance (Ghasemaghaei & Kordzadeh, 2024), testing whether transparency about power dynamics and process raises perceived fairness, even when distributions remain unchanged. This departs from parity-based metrics by explicitly modeling institutions, recourse, and structural asymmetries—offering a bridge between technical fairness and socio-legal critiques of power. If validated, it could reshape fairness-by-design to prioritize harms that are structurally hardest to absorb, not just statistically frequent.

References:

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