Machine learning (ML) systems have become an essential tool for experts of many domains, data scientists, and researchers, helping them to find answers to many complex business questions starting from raw datasets. Nevertheless, the development of ML systems able to satisfy the stakeholders’ needs requires an appropriate amount of knowledge about the ML domain.
Over the years, several solutions have been proposed to automate the development of ML systems. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, effectiveness, privacy, and others) is still missing.
In this presentation, we target the fairness and expressiveness quality attributes by proposing MANILA, a novel web application to support the development of fair and effective ML systems. In particular, MANILA guides the user in the selection of features always leading to an executable experiment and automatically identifies the setting (i.e., ML model and fairness-enhancing method) leading to the best fairness and effectiveness trade-off. MANILA is currently available as an application in the SoBigData Research Infrastracture and we present a demo of a use-case of MANILA applied in the social domain.