Accurate diagnosis of solid-appearing liver lesions on MRI scans is of utmost importance for subsequent treatment and prognosis. For example, the decision to perform a biopsy, surgery or chemotherapy is based on the findings of the radiologist. However, this diagnosis can be challenging, subjective, and time-consuming. This is partly due to the wide variety of lesions, differences in scan protocols and the rarity of certain subtypes. Artificial intelligence (AI) based on machine learning for automated liver lesion phenotyping has potential to realise breakthroughs in this domain. However, large amounts of clinically representative data are needed for training and testing of these models. To this end, the Liver Artificial Intelligence (LAI) consortium is creating a large-scale, clinically representative benchmark dataset. All consortium members can be found here: https://lai-consortium.org/ .