CounterfactualFairness

Counterfactual Fairness is a concept in algorithmic fairness that aims to ensure a decision made by a machine learning model would remain the same if a sensitive attribute of an individual had been different, all other relevant factors being equal. It's a causal inference-based approach to fairness. The core idea is to assess whether a model's outcome is dependent on a protected characteristic, such as race, gender, or age, by imagining a counterfactual scenario. For example, if a loan application is denied, counterfactual fairness asks if it would have been approved if the applicant belonged to a different demographic group but had identical qualifications and other non-sensitive attributes.

To achieve counterfactual fairness, one typically uses causal models to represent the relationships between attributes, decisions, and outcomes. These models allow for interventions, where specific attributes are hypothetically changed, and the resulting outcome is observed. If changing a sensitive attribute alters the model's prediction, the model is considered to be unfair according to this criterion. Counterfactual fairness offers a theoretically grounded definition of fairness that can be more robust than correlation-based measures in certain contexts, as it focuses on the direct causal impact of sensitive attributes. However, it can be challenging to implement in practice due to the difficulty in constructing accurate causal models and the need for data that supports counterfactual reasoning.