It is often suggested that ML is incompatible with data protection law, which is underpinned by principles like data minimization and purpose limitation. Data Protection by Design (DPbD), a core data protection requirement introduced in The General Data Protection Regulation (GDPR) similarly insists that these principles and other data protection imperatives be integrated into the processing of personal data. This whitepaper shows that in fact ML and data protection requirements, including principles like data minimization, are compatible.
It thus clears the path towards effective implementation of DPbD by offering data scientists guidance on embedding data protection principles within the life cycle of a machine learning model as well as clear instructions on how to fulfill the Data Protection by Design (DPbD) obligation and how to build a DPbD strategy in line with data protection principles.
