Fairness-Aware Multi-view Evidential Learning with Adaptive Prior

• URL: http://arxiv.org/abs/2508.12997v2
• Date: Sat, 08 Nov 2025 14:50:53 GMT
• Title: Fairness-Aware Multi-view Evidential Learning with Adaptive Prior
• Authors: Haishun Chen, Cai Xu, Jinlong Yu, Yilin Zhang, Ziyu Guan, Wei Zhao, Fangyuan Zhao, Xin Yang,
• Abstract summary: We propose Fairness-Aware Multi-view Evidential Learning (FAML) for multi-view evidential learning.<n>FAML achieves more balanced evidence allocation and improves both prediction performance and the reliability of uncertainty estimation.
• Score: 28.700062369310473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view evidential learning aims to integrate information from multiple views to improve prediction performance and provide trustworthy uncertainty esitimation. Most previous methods assume that view-specific evidence learning is naturally reliable. However, in practice, the evidence learning process tends to be biased. Through empirical analysis on real-world data, we reveal that samples tend to be assigned more evidence to support data-rich classes, thereby leading to unreliable uncertainty estimation in predictions. This motivates us to delve into a new Biased Evidential Multi-view Learning (BEML) problem. To this end, we propose Fairness-Aware Multi-view Evidential Learning (FAML). FAML first introduces an adaptive prior based on training trajectory, which acts as a regularization strategy to flexibly calibrate the biased evidence learning process. Furthermore, we explicitly incorporate a fairness constraint based on class-wise evidence variance to promote balanced evidence allocation. In the multi-view fusion stage, we propose an opinion alignment mechanism to mitigate view-specific bias across views, thereby encouraging the integration of consistent and mutually supportive evidence.Theoretical analysis shows that FAML enhances fairness in the evidence learning process. Extensive experiments on five real-world multi-view datasets demonstrate that FAML achieves more balanced evidence allocation and improves both prediction performance and the reliability of uncertainty estimation compared to state-of-the-art methods.

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