Abstract
Making machine learning systems interpretable can require access to information and resources. Whether that means access to data, to models, to executable programs, to research licenses, to validation studies, or more, various legal doctrines can sometimes get in the way. This talk will explain how intellectual property laws, privacy laws, and contract laws can block the access needed to implement interpretable machine learning, and will suggest avenues for reform to minimize these legal barriers.