Calibrated Explanations for Regression

Existing AI explanations don't quantify uncertainty: Current methods that explain individual AI predictions tell us which features matter most, but they fail to communicate how confident we should be in those explanations—a critical gap for responsible decision-making.

New method adds confidence intervals to feature importance: Calibrated Explanations for Regression provides uncertainty quantification around feature importance scores, meaning users can now see not just which factors influenced a prediction, but how reliable that information is.

Works across different prediction types: The method handles standard regression predictions (like predicting house prices) and probabilistic predictions (like estimating the probability a loan applicant will default above a certain threshold), making it broadly applicable to real-world decision support systems.

Enables both "what-if" and factual explanations: Users can understand why a specific prediction was made (factual) or explore how the prediction would change under different scenarios (counterfactual), supporting more informed human decision-making.

Model-agnostic and practical for deployment: Because the method works with any underlying AI model and provides fast, stable, and robust explanations, it can be integrated into existing decision support systems without requiring model redesign.