Explainability in machine learning: do popular methods deliver on their promises? (opens in new tab)

Ivona Cickovic and Andrea Serafino Machine learning models are increasingly used in organisational decision-making, yet their inner workings often remain opaque. When these systems influence real world outcomes, knowing what they predict is not enough – we also need to understand why. Explainability methods aim to illuminate this ‘black box,’ and feature attribution tools that … Continue reading Explainability in machine learning: do popular methods deliver on their promises? →