Processing and machine learning validation in FAIRiCUBE ensures that data workflows and algorithms used within the UCs are reliable, well-documented, and ethically sound. This includes validating data cleaning, transformation, and integration steps, as well as confirming that algorithms are implemented correctly and perform as expected. Benchmarking is used to compare performance across methods and datasets, while thorough documentation supports transparency and reproducibility. The validation process also pays attention to ethical considerations, including identifying and addressing potential biases in machine learning models.