Summary of The Model Openness Framework: Promoting Completeness and Openness For Reproducibility, Transparency, and Usability in Artificial Intelligence, by Matt White et al.
The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence
by Matt White, Ibrahim Haddad, Cailean Osborne, Xiao-Yang Yanglet Liu, Ahmed Abdelmonsef, Sachin Varghese, Arnaud Le Hors
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce the Model Openness Framework (MOF), a three-tiered classification system that rates machine learning models based on their transparency, reproducibility, and openness. The framework specifies code, data, and documentation components of the model development lifecycle that must be released under open licenses. A companion tool, the Model Openness Tool (MOT), provides a user-friendly implementation to evaluate the openness and completeness of models against the MOF classification system. By enhancing the openness and completeness of publicly-released models, the MOF aims to promote best practices in responsible AI research and development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to rate machine learning models based on how open they are. This is important because some people worry that these models might be used for bad things if we don’t know how they work or can’t see their code. The researchers created a framework called the Model Openness Framework (MOF) that has three levels. Each level has specific requirements for what needs to be shared, such as code and data. They also made a tool to help people check if models meet these openness standards. |
Keywords
* Artificial intelligence * Classification * Machine learning
