Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels

Counterfactual explanations work at three complementary levels: The paper identifies Local (individual decisions), Global (overall system patterns), and Group-wise (subpopulation trends) explanations as distinct but interconnected ways to understand AI systems, allowing stakeholders to choose the level of detail most relevant to their needs.

A unified method is more efficient than separate approaches: Rather than using different tools for each explanation level, the researchers developed a single gradient-based method that generates all three types simultaneously, which is particularly valuable for Group-wise explanations by combining grouping and explanation generation into one process instead of two separate steps.

"Plausibility" matters for trustworthy explanations: The paper introduces plausibility criteria to the Group-wise counterfactual domain, meaning explanations must be not just mathematically valid but also realistic and believable to users—addressing a key concern that overly technical or unrealistic "what-if" scenarios undermine trust in AI systems.

The method balances competing priorities in explanations: The approach successfully optimizes across validity (correct explanations), proximity (minimal changes to original scenarios), and plausibility (realistic suggestions), helping practitioners avoid trade-offs where improving one aspect damages another.

Practical validation demonstrates real-world applicability: The authors tested their method through practical use cases, suggesting the unified framework can move beyond theoretical contributions to actually help organizations explain AI decisions to different audiences—from individual users to regulators examining group-level fairness.