Modern NLP systems exhibit a range of biases, which a growing literature on model debiasing attempts to correct. However current progress is hampered by a plurality of definitions of bias, means of quantification, and oftentimes vague relation between debiasing algorithms and theoretical measures of bias. This paper seeks to clarify the current situation and plot a course for meaningful progress in fair learning, with two key contributions: (1) making clear inter-relations among the current gamut of methods, and their relation to fairness theory; and (2) addressing the practical problem of model selection, which involves a trade-off between fairness and accuracy and has led to systemic issues in fairness research. Putting them together, we make several recommendations to help shape future work.
翻译:现代国家劳工政策体系呈现了一系列偏见,关于模式贬低倾向的文献越来越多,试图纠正这些偏见;然而,目前的进展受到偏见、量化手段和偏向算法的多种定义的阻碍,而且往往模糊了偏向算法和理论偏向衡量方法之间的关系;本文件试图澄清目前的情况,并规划在公平学习方面取得有意义进展的路线,其中有两个关键贡献:(1) 明确现有各种方法之间的相互关系,以及它们与公平理论的关系;(2) 解决选择模式的实际问题,这涉及公平与准确之间的权衡,并导致公平研究中的系统性问题;将这些问题结合起来,我们提出若干建议,以帮助塑造今后的工作。