Explainable Machine Learning

Tim and Sayak discuss the challenges of achieving interpretability in machine learning models, exploring the importance of data quality and the potential of combining neural and symbolic approaches for more explainable models. They delve into the complexities of deep learning and the need for models that are not only accurate but also transparent and interpretable.