Causality-Aware Local Interpretable Model-Agnostic Explanations

Current explanation methods rely on unrealistic assumptions: Existing AI explanation tools assume features are independent from each other, which means they test how models behave with feature combinations that rarely or never occur in real data—potentially giving misleading explanations of how the AI actually works.

Causal relationships improve explanation accuracy: By incorporating knowledge about how variables actually cause or influence each other, the proposed method generates explanations that more faithfully replicate what the black-box model is actually doing, rather than approximating its behavior based on artificial scenarios.

Better explanations enable quality assessment: When explanations account for real causal relationships in data, users and stakeholders can more reliably evaluate whether the explanations are trustworthy and consistent—a critical requirement for responsible AI deployment in high-stakes decisions.

The improvement applies broadly across AI systems: Because this approach extends a "model-agnostic" explainer (one that works with any type of AI model), the benefits of incorporating causality can potentially be applied to many different kinds of AI systems currently in use.

Consistency and reliability are measurable: Experiments demonstrate that causal-aware explanations show improved "consistency and reliability," meaning the explanations remain stable and dependable across different scenarios—important for building justified trust in AI systems.