Counterfactual fairness refers to a concept in fairness in AI that evaluates whether a decision-making process would yield the same outcome if an individual’s sensitive attributes were altered while keeping everything else constant. This idea connects deeply to the need for fair and unbiased systems, ensuring that decisions are not unfairly influenced by characteristics like race or gender. By focusing on counterfactual scenarios, this approach allows us to identify and rectify biases within AI systems.
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