Towards Standardization of Data Licenses: The Montreal Data License

TL;DR

This paper introduces the Montreal Data License, a standardized framework for data licensing in AI and ML, aiming to improve transparency, clarity, and fairness in data usage through a new taxonomy and a web-based license generation tool.

Contribution

It proposes a novel taxonomy and license language for data licensing in AI/ML, along with a web tool for generating licenses based on this framework.

Findings

Developed the Montreal Data License (MDL) framework.

Created a web-based tool for license generation.

Enhanced clarity and transparency in data licensing.

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.