Counterfactual Instances Explain Little

Counterfactual explanations alone are insufficient: The paper argues that simply telling someone "if you had X instead of Y, you would get the desired outcome" doesn't actually explain why the system made its decision. Counterfactual instances by themselves provide minimal explanatory value without additional context.

Causal understanding is essential for real explanations: Satisfactory explanations require both counterfactual scenarios AND causal equations that show the underlying relationships between inputs and outputs. For example, knowing both "if your credit score were 50 points higher you'd get the loan" AND "because credit score directly influences lending decisions" provides genuine understanding.

Current XAI methods may be misleading users: Many explainable AI tools currently focus only on counterfactual instances (e.g., "change this feature to get a different outcome"), potentially giving people false confidence that they understand how a system works when they actually don't grasp the causal mechanisms involved.

Philosophy of science provides guidance for AI accountability: The paper draws on established philosophical principles about what constitutes a valid explanation, suggesting that responsible AI requires grounding explainability methods in rigorous epistemological standards rather than intuitive but incomplete approaches.

Better explanations require methodological improvements: XAI systems should be redesigned to incorporate causal reasoning alongside counterfactual information, ensuring that when organizations explain algorithmic decisions to affected individuals, those explanations are actually informative rather than superficially satisfying.