Ant: a process aware annotation software for regulatory compliance

• Published in: Artificial intelligence and law Vol. 32; no. 4; pp. 1075 - 1110
• Main Authors: Gyory, Raphaël, Restrepo Amariles, David, Lewkowicz, Gregory, Bersini, Hugues
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2024; Springer Nature B.V
• Subjects: Algorithms; Annotations; Artificial Intelligence; Business process engineering; Business process management; Compliance; Computer Science; Information Storage and Retrieval; Intellectual Property; IT Law; Legal Aspects of Computing; Machine learning; Media Law; Original Research; Philosophy of Law; Software; RegTech; Audit; Annotation tool; Legal-engineering cooperation; Machine learning; Regulatory compliance; GRC; Ground truth
• ISSN: 0924-8463; 1572-8382; 1572-8382
• DOI: 10.1007/s10506-023-09372-9

Accurate data annotation is essential to successfully implementing machine learning (ML) for regulatory compliance. Annotations allow organizations to train supervised ML algorithms and to adapt and audit the software they buy. The lack of annotation tools focused on regulatory data is slowing the adoption of established ML methodologies and process models, such as CRISP-DM, in various legal domains, including in regulatory compliance. This article introduces Ant, an open-source annotation software for regulatory compliance. Ant is designed to adapt to complex organizational processes and enable compliance experts to be in control of ML projects. By drawing on Business Process Modeling (BPM), we show that Ant can contribute to lift major technical bottlenecks to effectively implement regulatory compliance through software, such as the access to multiple sources of heterogeneous data and the integration of process complexities in the ML pipeline. We provide empirical data to validate the performance of Ant, illustrate its potential to speed up the adoption of ML in regulatory compliance, and highlight its limitations.