计算机与数字工程2025，Vol.53Issue(7)：1812-1816,5.DOI:10.3969/j.issn.1672-9722.2025.07.005
基于对抗性消除偏差的人工智能公平性决策增强算法
AI Fairness Decision Enhancement Algorithm Based On Adversarial Bias Elimination
摘要
Abstract
Artificial intelligence decision models often face ethical and legal challenges due to biases in training data,choice of algorithms,or their implementation,which not only may contravene social justice and legal norms but also may limit the univer-sality and quality assurance of the models.The paper presents a multidimensional adversarial debiasing method that employs adver-sarial learning mechanisms to enhance fairness and reduce biases in the models.Experimental results demonstrates that the multidi-mensional adversarial debiasing model achieves significant improvements of 7%～10%in fairness metrics,with reductions of 8%～11%in both equal opportunity difference and average odds parity difference.This paper applies adversarial learning to eliminate un-fairness in algorithmic decision-making,effectively balancing the model's predictive performance with fairness,and provides a sol-id theoretical foundation and practical pathway for developing more refined and practical fairness metrics and standardized fairness algorithms in the future.关键词
对抗性消除偏差技术/算法公平性/伦理与法律挑战/人工智能应用Key words
adversarial debiasing techniques/algorithmic fairness/ethical and legal challenges/AI applications分类
信息技术与安全科学引用本文复制引用
焦婉妮,刘浩锋..基于对抗性消除偏差的人工智能公平性决策增强算法[J].计算机与数字工程,2025,53(7):1812-1816,5.
