Documentation is officially defined as material that provides official information or evidence that serves as a record. From a machine learning perspective, particularly with regard to a deployed model in a production environment, documentation should serve as notes and descriptions to help us understand the model in its entirety. Ultimately, effective documentation makes our models understandable to the many stakeholders we interact with.
Whether we are deploying low-impact models serving basic and non-sensitive needs or high-impact models with significant outcomes, such as loan approvals, we have a responsibility to be transparent—not just to our end users, but to our internal stakeholders. Furthermore, it shouldn’t just be the data scientists who hold all knowledge underpinning a model—it should be open, accessible, and understandable to anyone.