Counterfactual explanations provide insights into how a decision made by a machine learning model might have changed if different inputs were used. These explanations are crucial for understanding model behavior, especially when decisions impact individuals significantly. By analyzing what would have happened under alternative scenarios, stakeholders can better comprehend the factors influencing decisions and enhance accountability.
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