  This paper provides a taxonomy for the licensing of data in the fields of
artificial intelligence and machine learning. The paper's goal is to build
towards a common framework for data licensing akin to the licensing of open
source software. Increased transparency and resolving conceptual ambiguities in
existing licensing language are two noted benefits of the approach proposed in
the paper. In parallel, such benefits may help foster fairer and more efficient
markets for data through bringing about clearer tools and concepts that better
define how data can be used in the fields of AI and ML. The paper's approach is
summarized in a new family of data license language - \textit{the Montreal Data
License (MDL)}. Alongside this new license, the authors and their collaborators
have developed a web-based tool to generate license language espousing the
taxonomies articulated in this paper.

Computers and Society;Machine Learning;Machine Learning