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This content will become publicly available on March 25, 2025

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

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.

 
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Award ID(s):
2147195 2239175
NSF-PAR ID:
10504017
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
AAAI
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
Volume:
38
Issue:
20
ISSN:
2159-5399
Page Range / eLocation ID:
22420 to 22428
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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