Page 198 - 《软件学报》2021年第5期

1422 Journal of Software 软件学报 Vol.32, No.5, May 2021

(3) 提供更高的准确性
构建公平且可靠的算法是可信机器学习算法的基础.
公平机器学习的第 5 个挑战是如何权衡算法性能与公平:当受保护属性与预测结果相关时,如累犯预测,很
难建立不包含与种族相关的分数,如果排除贫穷、失业和社会边缘化,准确率会下降.因此,我们需要进一步探索
权衡准确度和公平性的方式.

6 结 论

公平性是一种具有相对性的社会概念,绝对意义上的公平是不存在的.公平机器学习算法通过探索消除不
公平的机制,逐步完善机器学习算法的公平性.公平表征、公平建模和公平决策是可信机器学习公平性的 3 个
关键环节,有效定位并解决这 3 个环节的不公平问题,对未来公平机器学习算法的研究和发展具有重要意义.公
平具有在法律、社会层次的意义,不完全是一个技术问题,可信机器学习中的公平性研究可以认为是一个社会
学与计算机科学的交叉研究领域.在未来工作中,需要探究技术、应用和伦理等多方面的公平问题,部署先进的
公平机器学习算法于各应用领域,并形成统一且完整的公平性度量.

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