With the increasing pervasive use of machine learning in social and economic settings, there has been an interest in the notion of machine bias in the AI community. Models trained on historic data reflect biases that exist in society and propagated them to the future through their decisions. There are three prominent metrics of machine fairness used in the community, and it has been shown statistically that it is impossible to satisfy them all at the same time. This has led to an ambiguity with regards to the definition of fairness. In this report, a causal perspective to the impossibility theorem of fairness is presented along with a causal goal for machine fairness.
翻译:随着在社会和经济环境中日益普遍使用机器学习,AI社区对机器偏见的概念一直很感兴趣,在历史数据方面受过培训的模型反映了社会存在的偏见,并通过其决定向未来传播这些偏见。在社区中,有三种重要的机器公平标准,从统计上表明不可能同时满足所有标准,这导致在公平定义方面出现模糊。在本报告中,对不可能实现的公平理论提出了因果关系观点,同时提出了实现机器公平的目标。