Nowadays, many decisions are made using predictive models built on historical
data.Predictive models may systematically discriminate groups of people even if
the computing process is fair and well-intentioned. Discrimination-aware data
mining studies how to make predictive models free from discrimination, when
historical data, on which they are built, may be biased, incomplete, or even
contain past discriminatory decisions. Discrimination refers to disadvantageous
treatment of a person based on belonging to a category rather than on
individual merit. In this survey we review and organize various discrimination
measures that have been used for measuring discrimination in data, as well as
in evaluating performance of discrimination-aware predictive models. We also
discuss related measures from other disciplines, which have not been used for
measuring discrimination, but potentially could be suitable for this purpose.
We computationally analyze properties of selected measures. We also review and
discuss measuring procedures, and present recommendations for practitioners.
The primary target audience is data mining, machine learning, pattern
recognition, statistical modeling researchers developing new methods for
non-discriminatory predictive modeling. In addition, practitioners and policy
makers would use the survey for diagnosing potential discrimination by
predictive models.

本文综述了关于如何消除历史数据中偏见和歧视对预测模型造成的影响的研究，包括综述了各种可用于测量数据中歧视的方法和评估反歧视预测模型性能的措施，并提出了非歧视性预测建模的实践建议。