EVO-LRP: Evolutionary Optimization of LRP for Interpretable Model Explanations

Current XAI methods rely on arbitrary choices: Traditional Layer-wise Relevance Propagation (LRP) implementations use heuristic rule sets that aren't optimized for clarity, meaning the explanations we get from AI systems may not be the best possible explanations—they're just convenient ones.

Systematic optimization improves explanation quality: EVO-LRP uses evolutionary algorithms to automatically tune LRP settings based on measurable interpretability metrics like "faithfulness" (how accurately the explanation reflects what the model actually uses) and "sparseness" (how focused the explanation is), demonstrating that better explanations can be engineered rather than assumed.

One-size-fits-all explanations are suboptimal: The research shows EVO-LRP has "strong sensitivity to class-specific features," meaning different types of predictions may require different explanation approaches—a single explanation method won't work equally well for all AI decisions.

Explanation quality can be measured and compared: By using quantitative metrics to evaluate interpretability, this work establishes that we can objectively assess whether one explanation method is better than another, moving XAI from subjective judgment toward measurable standards.

Better explanations require intentional design: The finding that "attribution quality can be systematically improved through principled, task-specific optimization" suggests that responsible AI requires deliberate effort to make explanations clear and trustworthy, rather than accepting default options.