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Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on peoples 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, in particular 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 applicability depends heavily on the identifiability of those quantities from observational data. In this paper, we compile the most relevant identifiability criteria for the problem of fairness from the extensive literature on identifiability theory. These criteria are then used to decide about the applicability of causal-based fairness notions in concrete discrimination scenarios.
ML-based predictive systems are increasingly used to support decisions with a critical impact on individuals lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requ
If our models are used in new or unexpected cases, do we know if they will make fair predictions? Previously, researchers developed ways to debias a model for a single problem domain. However, this is often not how models are trained and used in prac
The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is
Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently. So far the existing research has been mostly focusing on the development of quantitative metrics to measure algorithm disparities across
In machine learning (ML) applications, unfair predictions may discriminate against a minority group. Most existing approaches for fair machine learning (FML) treat fairness as a constraint or a penalization term in the optimization of a ML model, whi