Towards Standardization of Data Licenses: The Montreal Data License
By Misha Benjamin, P. Gagnon, Negar Rostamzadeh, C. Pal, Yoshua Bengio, Alex Shee. 2019
41 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems and Management, AI Ethics, Fairness & Society
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alexandra Sasha Luccioni
- Dragomir R. Radev
- Noah A. Smith
- Rishi Bommasani
- Kyle Lo
- Yejin Choi
- Yacine Jernite
- Mohit Bansal
In Information Systems and Management
In AI Ethics, Fairness & Society
- Alexandra Sasha Luccioni
- Jingyi Yang
- Zeerak Talat
- Mike Ananny
- Yacine Jernite
- Margaret Mitchell
- Inioluwa Deborah Raji
- Bertie Vidgen
Citations in the corpus
- Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022, methodology): “3445922 [13] Misha Benjamin, Paul Gagnon, Negar Rostamzadeh, Chris Pal, Yoshua Bengio, and Alex Shee.”
- Behavioral Use Licensing for Responsible AI (2020, methodology): “For example, the Montreal Data License Generator [15] uses a questionnaire to generate intellectual property (IP) licensing terms that can be attached to datasets to govern its distribution.”
- A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication (2021, background): “This has brought attention to the importance of conscientious data stewardship practices in all steps of the dataset life cycle, ranging from proper documentation at the data creation stage via checklists [58] and data sheets [34] to the study of dataset usage and genealogy [32, 50], licensing and a”
- ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles (2019, background): “Some work has gone further to suggest a legal advantage to declaring intended use cases and ethical concerns, as it can provide grounds for legally restricting third party misuse [3].”