In Artificial intelligence in medicine ; h5-index 34.0
Increasingly complex learning methods such as boosting, bagging and deep learning have made ML models more accurate, but harder to interpret and explain, culminating in black-box machine learning models. Model developers and users alike are often presented with a trade-off between performance and intelligibility, especially in high-stakes applications like medicine. In the present article we propose a novel methodological approach for generating explanations for the predictions of a generic machine learning model, given a specific instance for which the prediction has been made. The method, named AraucanaXAI, is based on surrogate, locally-fitted classification and regression trees that are used to provide post-hoc explanations of the prediction of a generic machine learning model. Advantages of the proposed XAI approach include superior fidelity to the original model, ability to deal with non-linear decision boundaries, and native support to both classification and regression problems. We provide a packaged, open-source implementation of the AraucanaXAI method and evaluate its behaviour in a number of different settings that are commonly encountered in medical applications of AI. These include potential disagreement between the model prediction and physician's expert opinion and low reliability of the prediction due to data scarcity.
Parimbelli Enea, Buonocore Tommaso Mario, Nicora Giovanna, Michalowski Wojtek, Wilk Szymon, Bellazzi Riccardo
2023-Jan
Black-box, Explainable, Explanation, Fidelity, Interpretable, Local explanation, Model agnostic, Post-hoc, Reliability, Surrogate model, XAI
