# Towards Standardization of Data Licenses: The Montreal Data License

arXiv: https://arxiv.org/abs/1903.12262 · HTML: https://mlsanity.com/paper/1903.12262/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2019.

## Built on by (most cited first, 19 of 19)

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- ["They've Stolen My GPL-Licensed Model!": Toward Standardized and Transparent Model Licensing](https://mlsanity.com/paper/2412.11483/) (2024)
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- [LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance](https://mlsanity.com/paper/2501.00106/) (2024)
- [An investigation of licensing of datasets for machine learning based on the GQM model](https://mlsanity.com/paper/2303.13735/) (2023)
- [FAIRLABEL: Correcting Bias in Labels](https://mlsanity.com/paper/2311.00638/) (2023)
- [On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning](https://mlsanity.com/paper/2401.10304/) (2024)
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- [SimClone: Detecting Tabular Data Clones using Value Similarity](https://mlsanity.com/paper/2407.12802/) (2024)

## Abstract (arXiv)

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 - textitthe 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.