Summary of Data Authenticity, Consent, & Provenance For Ai Are All Broken: What Will It Take to Fix Them?, by Shayne Longpre et al.
Data Authenticity, Consent, & Provenance for AI are all broken: what will it take to fix them?
by Shayne Longpre, Robert Mahari, Naana Obeng-Marnu, William Brannon, Tobin South, Katy Gero, Sandy Pentland, Jad Kabbara
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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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 novel paper investigates the limitations of foundation models, which are fueled by massive, yet under-documented training datasets. The current practices in collecting such data have led to issues with tracing authenticity, obtaining consent, preserving privacy, addressing representation and bias, respecting copyright, and developing ethical foundation models. To address these concerns, policymakers emphasize the importance of transparency in training data collection. This paper analyzes the landscape of foundation model training data and existing solutions, identifying a missing infrastructure for responsible foundation model development practices. It highlights the shortcomings of common tools for tracing data authenticity, consent, and documentation, providing insights on how to adopt universal data provenance standards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models rely heavily on massive datasets, but these collections are often under-documented and lacking transparency. This lack of transparency raises concerns about authenticity, consent, privacy, representation, bias, copyright, and ethical foundation model development. Policymakers want more transparency in training data collection to understand foundation models’ limitations. This paper looks at the big picture of foundation model training data and existing solutions, finding a missing piece for responsible foundation model development practices. It shows how common tools fail to provide accurate information about data authenticity, consent, and documentation. |
