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NLP系统展现出多种偏见，越来越多的模型去偏研究正试图纠正这些问题。然而，当前进展受到诸多因素的阻碍，包括偏见的定义多样性、量化手段的差异，以及去偏算法与理论偏见度量之间常有的模糊关系。本文旨在澄清当前状况，并为有意义的公平学习进展规划路径，主要贡献有二：（1）厘清当前各类方法之间的相互关系及其与公平性理论的联系；（2）解决模型选择的实际难题，该问题涉及公平性与准确性的权衡，并已导致公平性研究中的系统性问题。综合上述贡献，我们提出若干建议以指导未来研究方向。