Mitigating governance impact on machine learning

Abstract

A method, a computerized apparatus and a computer program product for mitigating governance and regulation implications on machine learning. A governance impact assessment is generated for a partial data set generated by applying a data governance enforcement on a data set of instances comprising valuations of a feature vector. The partial data set comprises partial instances each comprising partial feature vectors. The governance impact assessment comprises information about data excluded from the data set. A machine learning model trained based on the partial data set and configured to provide an estimated prediction for a partial instance is obtained. A set of core features is determined. A bias introduced by the data governance is identified based on a core feature being affected by the data governance. In response to identifying a bias, an anti-bias procedure is applied on the machine learning model, whereby mitigating the bias introduced by the data governance.