Test page one. As machine learningthis is a test tooltip becomes central to many decision-making processestest including high-stakes decisions in criminal justice, healthcare, and banking$definition test organizations using ML systems to aid or automate decisions face increased pressure for transparency on how these decisions are made. In a 2019 Harvard Business Review article, Eric Colson states that routine decisions based on structured data are best handled by artificial intelligence as AI is “less prone to human’s cognitive bias.” However, the author goes on to warn, developers and deployers of AI, specifically ML systems, should consider the inherent “risk of using biased data that may cause AI to find specious relationships that are unfair.” Annotation and Benchmarking on Understanding and Transparency of Machine learning Lifecycles (ABOUT ML) is a project of the Partnership on AI (PAI) working towards establishing new norms on transparency by identifying best practices for documenting and characterizing key components and phases throughout the ML system lifecycle from design to deployment, including annotations of data, algorithms, performance, and maintenance requirements.