Summary of Usage Governance Advisor: From Intent to Ai Governance, by Elizabeth M. Daly et al.
Usage Governance Advisor: From Intent to AI Governance
by Elizabeth M. Daly, Sean Rooney, Seshu Tirupathi, Luis Garces-Erice, Inge Vejsbjerg, Frank Bagehorn, Dhaval Salwala, Christopher Giblin, Mira L. Wolf-Bauwens, Ioana Giurgiu, Michael Hind, Peter Urbanetz
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed paper focuses on evaluating the safety of AI systems, a crucial concern for organizations deploying them. The lack of fairness in AI models can lead to societal damage, legal repercussions, and reputational damage. Safety encompasses both what a model does (e.g., revealing personal information) and how it was built (e.g., training on licensed data sets). To determine AI system safety, one must gather information from various sources, including safety benchmarks and technical documentation. The paper presents the Usage Governance Advisor, which generates semi-structured governance information, identifies risks, recommends benchmarks, and proposes mitigation strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems need to be safe to use! The problem is that some AI models can reveal personal information or cause other problems. To make sure this doesn’t happen, we need to evaluate the safety of these systems. This means looking at what the model does and how it was made. We also need to gather information from many different places, like special benchmarks and technical documents. The paper introduces a tool called Usage Governance Advisor that helps with all this. |
