Fighting the disagreement in Explainable Machine Learning with consensus

Understand

Machine learning (ML) models are often valued by the accuracy of their predictions.

• However, in some areas of science, the inner workings of models are as relevant as their accuracy.
• To understand how ML models work internally, the use of interpretability algorithms is the preferred option.
• Unfortunately, despite the diversity of algorithms available, they often disagree in explaining a model, leading to contradictory explanations.