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What is the specific goal of using counterfactual fairness metrics when auditing a decision-making model for discriminatory behavior?

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The specific goal of using counterfactual fairness metrics is to determine if a decision-making model would produce the same outcome for an individual if their protected attribute, such as race, gender, or age, were different while all other relevant circumstances remained unchanged. A counterfactual is a hypothetical scenario that asks what would have happened if a specific condition were altered. In the context of algorithmic auditing, this approach isolates the causal effect of a protected characteristic on a decision. To audit a model using this metric, an evaluator creates a counterfactual twin of an individual by keeping their non-sensitive features, such as education or employment history, constant while flipping their sensitive attribute. If the model provides a different result for the original individual and their counterfactual twin, the model is considered to have violated counterfactual fairness because the decision was influenced by the protected attribute rather than legitimate criteria. This method is effective because it moves beyond mere statistical correlation to examine direct causal relationships, helping developers identify and eliminate biases that are baked into the decision-making logic of an artificial intelligence system.

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What is the specific goal of using counterfactual fairness metrics when auditing a decision-making model for discriminatory behavior?

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