Summary of Mitigating Downstream Model Risks Via Model Provenance, by Keyu Wang et al.
Mitigating Downstream Model Risks via Model Provenance
by Keyu Wang, Abdullah Norozi Iranzad, Scott Schaffter, Meg Risdal, Doina Precup, Jonathan Lebensold
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 A new approach is proposed to manage foundation model-based systems, which are rapidly advancing in research and industry. The current tools for understanding the provenance and lineage of models fall short, particularly in tracing genealogy, enabling machine readability, and providing reliable centralized management systems. This challenge mirrors software supply chain security issues but AI/ML is at an earlier stage. To address this, a machine-readable model specification format is introduced to simplify the creation of model records, reducing human effort and enhancing transparency across the lifecycle. The unified model record (UMR) repository is also proposed as a semantically versioned system that automates publication to multiple formats and provides a hosted web interface. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are advancing rapidly in research and industry, but current tools for understanding their provenance and lineage fall short. This paper proposes a new approach to manage these models by introducing a machine-readable model specification format and the unified model record (UMR) repository. The UMR repository automates the publication of model records to multiple formats and provides a hosted web interface. |
