This theme aims to address challenges related to:
Transparency of Machine Learning (ML)
How to construct transparent ML algorithms that are not only predictive, but also provide an acceptable degree of interpretability with quantifiable uncertainty?
Low quality data
How to address issues involving large amounts of, potentially low-quality, data, whereby, in addition to algorithmic efficiency and uncertainty quantification, existing domain-specific factors need to be considered?