Energy Shields for Fairness
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Abstract (arXiv)
Runtime fairness is not a one-time constraint but a dynamic property evaluated over a sequence of decisions. To ensure fairness at runtime, it is necessary to account for past decisions, information neglected by conventional, static classifiers. Traditional fairness shields enforce runtime fairness abruptly, by intervening deterministically whenever a sequence of decisions violates the target for a running fairness measure. This motivates our emphmain conceptual contribution: energy shields. An energy shield is a novel, lightweight, adaptive controller that monitors a sequence of decisions and intervenes probabilistically to ensure runtime fairness smoothly, by utilizing physics-inspired energy functions to nudge the sequence toward fairness: the more unfair the decisions, the stronger the nudging force becomes. This makes energy shields the emphfirst fairness shields to provide both short-term safety and long-term liveness guarantees. Safety ensures that the running fairness measure stays within a running target interval with high probability, and liveness ensures that the limit of the fairness measure lies within the limit target interval. Intuitively, the short-term specifies the tolerated fairness values and the long-term specifies the desired fairness values. We also provide a synthesis procedure for constructing the least intrusive energy shield for a given target specification, and demonstrate its efficiency experimentally. We evaluate our energy shields against existing fairness shields through the lens of short- and long-term fairness.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 24 May 2026.
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24 of 49 references.
References · 24
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2014Certifying and removing disparate impactcited by 715
- 2018A Reductions Approach to Fair Classificationcited by 587
- 2017Safe Reinforcement Learning via Shieldingcited by 449
- 2020Fairness in Machine Learning: A Surveycited by 370
- 2018Delayed Impact of Fair Machine Learningcited by 184
- 2021Fair Mixup: Fairness via Interpolationcited by 109
- 2019Algorithms for Fairness in Sequential Decision Makingcited by 56
- 2022Safe Reinforcement Learning via Shielding under Partial Observabilitycited by 56
- 2018Probabilistic Verification of Fairness Properties via Concentrationcited by 45
- 2023Safe Reinforcement Learning via Probabilistic Logic Shieldscited by 42
12 more
- 2017Fair Kernel Learningcited by 39
- 2020Justicia: A Stochastic SAT Approach to Formally Verify Fairnesscited by 25
- 2021Certifying Robustness to Programmable Data Bias in Decision Treescited by 21
- 2021Probabilistic Verification of Neural Networks Against Group Fairnesscited by 17
- 2023Runtime Monitoring of Dynamic Fairness Propertiescited by 17
- 2023Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applicationscited by 15
- 2017Real-time Stream-based Monitoringcited by 13
- 2023Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Makingcited by 8
- 2023Certifying the Fairness of KNN in the Presence of Dataset Biascited by 7
- 2024Fairness Shields: Safeguarding against Biased Decision Makerscited by 5
- 2025Stream-Based Monitoring of Algorithmic Fairnesscited by 5
- 2025Monitoring Robustness and Individual Fairnesscited by 3
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