Counterfactual Fairness

Counterfactual fairness is a concept in artificial intelligence that ensures decisions or predictions are fair by asking: "Would the outcome have been the same if the individual's protected characteristics (like race, gender, or age) were different?" It aims to prevent biases linked to these attributes from influencing decisions. Essentially, it evaluates whether a model's output remains consistent in hypothetical scenarios where only the protected characteristic varies, ensuring that people are treated fairly regardless of personal backgrounds. This approach helps create more equitable systems by minimizing biased influences.