Survey on Causal-based Machine Learning Fairness Notions

Abstract

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.

References in corpus (5)

• Matching Methods for Causal Inference: A Review and a Look Forward
• Equality of Opportunity in Supervised Learning
• Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality
• Testing Identifiability of Causal Effects
• What Counterfactuals Can Be Tested

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• Towards Fair Graph Neural Networks via Graph Counterfactual
• Counterfactual Learning on Graphs: A Survey
• Improvement-Focused Causal Recourse (ICR)
• Fairness Through Counterfactual Utilities
• Causal Learning for Socially Responsible AI