The compatibility of machine learning (ML) with transparency requirements—especially in the context of competition law and regulatory oversight—presents a complex interplay of technical, ethical, and legal considerations. The frequent reference to ML systems as “black boxes” reflects the difficulty stakeholders often face in understanding, interpreting, and governing the decisions made by these systems. To address