Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc. Several notions of fairness have been defined and examined in the past decade, such as statistical parity and equalized odds. The most recent fairness notions, however, are causal-based and reflect the now widely accepted idea that using causality is necessary to appropriately address the problem of fairness. This paper examines an exhaustive list of causal-based fairness notions and study their applicability in real-world scenarios. As the majority of causal-based fairness notions are defined in terms of non-observable quantities (e.g., interventions and counterfactuals), their deployment in practice requires to compute or estimate those quantities using observational data. This paper offers a comprehensive report of the different approaches to infer causal quantities from observational data including identifiability (Pearl's SCM framework) and estimation (potential outcome framework). The main contributions of this survey paper are (1) a guideline to help selecting a suitable fairness notion given a specific real-world scenario, and (2) a ranking of the fairness notions according to Pearl's causation ladder indicating how difficult it is to deploy each notion in practice.
翻译:解决公平问题对于安全地使用机算学习算法支持对人们生活有重大影响的决策至关重要,例如雇用工作、虐待儿童、疾病诊断、贷款等。过去十年中,界定和审查了若干公平概念,例如统计均等和均等差数。但最近的一些公平概念基于因果关系,反映了目前广泛接受的关于必须使用因果关系来适当解决公平问题的观点。本文件审查了一个详尽的基于因果关系的公平概念清单,并研究了这些概念在现实世界情景中的适用性。由于大多数基于因果关系的公平概念是以不可观察的数量(例如干预和反事实)来界定的,因此在实际中部署这些公平概念需要用观察数据来计算或估计这些数量。本文件全面报告了从观察数据(包括识别能力(Pearl's SCM框架))和估计(潜在成果框架)中推算因果关系的不同方法。本调查文件的主要贡献是:(1) 指导准则,帮助根据具体的真实世界情景选择适当的公平概念(例如干预和反事实),因此,实际应用这些概念的实际需要用观察数据来计算或估计这些数量。本文件提供了一份全面的报告,说明了从观察数据中推算出因数量的不同方法,从观察数据中推算出因果关系,包括可辨别(Pearl's s Sure's s s s s s s s s s s slight slist laction sliction sliction liction liction lipplictions lipple) a lictions is lictions is lictions is lictions is lippin