midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

MID creates simplified, interpretable versions of complex black-box models by decomposing them into low-order additive representations—meaning the package helps translate opaque AI predictions into understandable mathematical formulas that stakeholders can actually read and verify.

The method balances accuracy with interpretability by minimizing the difference between the original black-box model's predictions and the simplified surrogate model, ensuring that the explanation doesn't sacrifice too much predictive fidelity in the process of becoming understandable.

This tool addresses a critical barrier to responsible AI adoption in high-stakes fields like healthcare, finance, and criminal justice, where regulations and ethical standards increasingly require that organizations can explain why their AI systems made specific decisions—not just that they work.

The midr R package makes this interpretability technique accessible to practitioners without requiring deep machine learning expertise, democratizing the ability to audit and understand black-box models across different organizations and industries.

Global surrogate models like MID enable comprehensive model auditing rather than just explaining individual predictions, allowing organizations to identify systematic biases, fairness issues, or unexpected patterns in how their AI systems behave across different populations or scenarios.