Measuring Epistemic Unfairness for Algorithmic Decision-Making

2026-04-24

AI Summary

This study addresses a critical gap in algorithmic fairness auditing by shifting focus from prediction-level disparities to cognitive injustices—systematic deprivations of users’ credibility, epistemic uptake, and cognitive agency induced by algorithmic mediation. It introduces the first computable framework grounded in theories of cognitive justice, modeling the divergence between ideal and actual epistemic conditions. The framework distinguishes two evaluative dimensions: resource inequality and inequality in capabilities or rights. Integrating an allocative fairness index with a deficit-template methodology, the approach enables longitudinal audits of opinion dynamics within recommender systems. Empirical results demonstrate that even algorithms satisfying conventional fairness constraints can perpetuate distinct patterns of cognitive harm, such as exclusionary tail effects and hierarchical concentration, thereby offering practitioners a concrete, actionable toolkit for assessing and mitigating cognitive unfairness in algorithmic design.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Algorithmic accountability and transparency on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Algorithmic systems increasingly function as epistemic infrastructures that govern the conditions of interpretative access and social belief. Yet, mainstream auditing strategies operationalize fairness primarily in predictive terms - error rates, calibration, or group-level parity - leaving epistemic harms under-theorized and under-measured. We propose a quantitative framework for evaluating forms of epistemic injustice in algorithmic environments. First, we introduce a deficit-based template that models epistemic injustices as gaps between ideal and realized conditions across features such as credibility, uptake, and epistemic agency. We map these deficits to concrete stages of algorithmic mediation, showing how epistemic injustice can persist even when standard fairness constraints are satisfied. Drawing on distributive fairness indices, we distinguish two evaluation stances: resource inequality, where indices are applied to distributions of epistemic goods directly, and capability/rights inequity, where indices are applied to output-induced epistemic opportunity. We provide an epistemic translation of canonical indices, illustrating how they diagnose complementary signatures of unfairness - such as exclusionary tails and hierarchical concentration - and support longitudinal auditing under iterative deployment. We also provide a simulation study of a recommender-mediated opinion dynamics setting, showing how the proposed indices capture the evolution of epistemic unfairness under repeated platform interventions. The result is a measurement framework that makes the epistemic dimension of algorithmic harms explicit for system design and evaluation.

Problem

• epistemic injustice
• algorithmic fairness
• epistemic unfairness
• fairness auditing
• algorithmic decision-making

Innovation

• epistemic injustice
• algorithmic fairness
• distributive fairness indices
• epistemic agency
• longitudinal auditing