The transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) systems continues to impact society, propelled by advancements in Natural Language Processing (NLP) and Computer Vision (CV). These technologies have seen significant progress in recent years, leading to a surge in their applications and capabilities. These technologies, which leverage vast datasets to learn patterns through predictive models, herald an era of enhanced automation and decision-making capabilities. However, the performance of AI models is linked not only to engineering aspects, but also to the data quality and the governance frameworks that guide how this data is collected, used and managed.
This report explores the broader network of data interactions and the roles of various stakeholders. It highlights the importance of collaboration among, often multidisciplinary, practitioners who develop AI/ML systems, including data scientists, engineers, domain experts, and other stakeholders all contributing to understanding and governing data in different ways.
