Title: The ethical ambiguity of AI data enrichment:  Measuring gaps in research ethics norms and practices. Abstract: The technical progression of artificial intelligence (AI) research has been built on breakthroughs in fields such as computer science, statistics, and mathematics. However, in the past decade AI researchers have increasingly looked to the social sciences, turning to human interactions to solve the challenges of model development. Paying crowdsourcing workers to generate or curate data, or ‘data enrichment’, has become indispensable for many areas of AI research, from natural language processing to inverse reinforcement learning. Other fields that routinely interact with crowdsourcing workers, such as Psychology, have developed common governance requirements and norms to ensure research is undertaken ethically. This study explores how, and to what extent, comparable research ethics requirements and norms have developed for AI research and data enrichment. We focus on the approach taken by two leading AI conferences: ICLR and NeurIPS. In a longitudinal study of accepted papers, and a comparison with Springer journal articles and Psychology papers, this work finds that ICLR and NeurIPS have established protocols for human data collection which are inconsistently followed by authors. Whilst Psychology papers engaging with crowdsourcing workers frequently disclose ethics reviews, payment data, demographic data and other information, such disclosures are far less common in leading AI conferences despite similar guidance. The work concludes with hypotheses to explain these gaps in research ethics practices and considerations for its implications.