# SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models

arXiv: https://arxiv.org/abs/2401.15270 · HTML: https://mlsanity.com/paper/2401.15270/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 37 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Improving Variational Inference with Inverse Autoregressive Flow](https://mlsanity.com/paper/1606.04934/) (2016)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [Fairness in Deep Learning: A Computational Perspective](https://mlsanity.com/paper/1908.08843/) (2019)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases](https://mlsanity.com/paper/2010.15052/) (2020)
- [SensitiveLoss: Improving Accuracy and Fairness of Face Representations with Discrimination-Aware Deep Learning](https://mlsanity.com/paper/2004.11246/) (2020)
- [Towards Fair Deep Anomaly Detection](https://mlsanity.com/paper/2012.14961/) (2020)
- [Transferring Fairness under Distribution Shifts via Fair Consistency Regularization](https://mlsanity.com/paper/2206.12796/) (2022)
- [Adaptive Fairness-Aware Online Meta-Learning for Changing Environments](https://mlsanity.com/paper/2205.11264/) (2022)
- [FairST: Equitable Spatial and Temporal Demand Prediction for New Mobility Systems](https://mlsanity.com/paper/1907.03827/) (2019)

## Built on by (most cited first, 4 of 4)

- [Knowledge-guided Machine Learning: Current Trends and Future Prospects](https://mlsanity.com/paper/2403.15989/) (2024)
- [Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science](https://mlsanity.com/paper/2502.06084/) (2025)
- [FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning](https://mlsanity.com/paper/2412.10669/) (2024)
- [Foundation Models for Environmental Science: A Survey of Emerging Frontiers](https://mlsanity.com/paper/2504.04280/) (2025)

## Abstract (arXiv)

Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due to the change in distribution over different regions. While techniques have been developed to improve the transferability of fairness, a solution to the problem is not always feasible with no samples from the new regions, which is a bottleneck for pure data-driven attempts. Fortunately, physics-based mechanistic models have been studied for many problems with major social impacts. We propose SimFair, a physics-guided fairness-aware learning framework, which bridges the data limitation by integrating physical-rule-based simulation and inverse modeling into the training design. Using temperature prediction as an example, we demonstrate the effectiveness of the proposed SimFair in fairness preservation.