The concept of data ethics is being reevaluated by a group of AI researchers in Africa, who argue that the origin, collection, and sharing of data are often overlooked but critical components of AI ethics. They highlight issues such as deficit narratives, extractive data practices, and moral distance between data collectors and communities, which can lead to harm and mistrust. The authors emphasize the importance of building trust, respecting local norms and contexts, and ensuring that data is shared in a way that benefits both communities and science. They also argue that AI ethics must start with data collection and sharing, rather than just model development, to ensure fairness, equity, and justice in AI systems.