Towards Piece-by-Piece Explanations for Chess Positions with SHAP

Chess engines can now be made more transparent: The paper demonstrates that SHAP, a leading explainability technique from AI research, can successfully break down opaque chess engine evaluations into understandable contributions from individual pieces, making it possible to see why an engine thinks a position is winning or losing rather than just that it is.

Explainability methods can be adapted across domains: By treating chess pieces as "features" to be analyzed—similar to how SHAP analyzes features in other AI systems—the researchers show that responsible AI techniques developed in one field can be meaningfully applied to specialized domains, potentially improving transparency in other complex decision-making systems.

Human-interpretable AI aligns with established expertise: The approach mirrors how chess masters have traditionally learned to evaluate positions by mentally removing pieces, suggesting that making AI systems more explainable often means connecting them to how human experts already think about problems, rather than imposing entirely foreign frameworks.

Transparency enables better human oversight and learning: By visualizing which pieces contribute most to an engine's evaluation, the method creates opportunities for chess players to better understand engine reasoning, train more effectively, and potentially catch errors—demonstrating how explainability supports human-AI collaboration rather than blind reliance on AI outputs.

Open research accelerates responsible AI development: The authors' commitment to releasing code and data encourages other researchers to build on this work, exemplifying how transparency in AI research itself—not just in AI systems—helps develop more trustworthy and interpretable technologies.