ServiceNow Research

Towards Standardization of Data Licenses: The Montreal Data License

Abstract

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.

Publication
Workshop at the International Conference on Learning Representations (ICLR)
Christopher Pal
Christopher Pal
Distinguished Scientist

Distinguished Scientist at Low Data Learning located at Montreal, QC, Canada.

Yoshua Bengio
Yoshua Bengio
Research Advisor

Research Advisor at Human Decision Support located at Montreal, QC, Canada.