In recent years, the problem of addressing fairness in Machine Learning (ML) and automatic decision-making has attracted a lot of attention in the scientific communities dealing with Artificial Intelligence. A plethora of different definitions of fairness in ML have been proposed, that consider different notions of what is a "fair decision" in situations impacting individuals in the population. The precise differences, implications and "orthogonality" between these notions have not yet been fully analyzed in the literature. In this work, we try to make some order out of this zoo of definitions.

翻译：近年来,解决机器学习(ML)和自动决策中的公正性问题引起了处理人造情报的科学界的极大关注,提出了许多关于人造情报的关于公平性的不同定义,其中考虑到在影响人口中个人的情况中什么是“公平决定”的不同概念。这些概念之间的确切差异、影响和“异同性”尚未在文献中得到充分分析。在这项工作中,我们试图从定义的这个园中理出一些条理。