  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.
