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Why Algorithms Need Temporal Fairness
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DOI：10.1109/ACCESS.2025.3638361.png)
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Abstract
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Automated decision-making systems are increasingly entrusted with life-defining decisions, yet prevailing fairness frameworks largely treat these systems as static, one-shot processes. This creates a critical gap: existing methods fail to capture how repeated decisions accumulate, allowing small disparities to evolve into lasting inequities. Fairness, however, is inherently temporal. A single loan denial can delay credit building for years, while repeated résumé rejections can systematically exclude qualified candidates. To address this limitation, a unified mathematical framework for temporal fairness is introduced, evaluating fairness not as a snapshot but as an evolving trajectory of opportunities and outcomes. The framework assesses whether individuals and groups progress at comparable rates or face persistent exclusion, revealing lock-out effects, widening opportunity gaps, and delays in access. Interpretable temporal metrics such as cumulative access, lock-out rate, and time-to-first-approval make long-term fairness measurable and auditable. Theoretical analysis and empirical validation demonstrate how feedback loops can either reinforce inequities or foster inclusion, and how short-term parity may conflict with long-term equity. By reframing fairness as a temporal property, this work provides both theoretical foundations and empirically grounded tools for developing decision systems that are transparent, adaptive, and sustainable in their equity commitments.
Keywords:
Algorithmic bias
automated decision-making
fairness metrics
long-term equity
machine learning ethics
temporal fairness
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