A new method for decomposing your favorite performance metrics
Co-authored with S. Hué, C. Hurlin, and C. Pérignon.
Trustability and acceptability of sensitive AI systems largely depend on the capacity of the users to understand the associated models, or at least their forecasts. To lift the veil on opaque AI applications, eXplainable AI (XAI) methods such as post-hoc interpretability tools (e.g. SHAP, LIME), are commonly utilized today, and the insights generated from their outputs are now widely comprehended.
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