Summary of Ml-eat: a Multilevel Embedding Association Test For Interpretable and Transparent Social Science, by Robert Wolfe et al.
ML-EAT: A Multilevel Embedding Association Test for Interpretable and Transparent Social Science
by Robert Wolfe, Alexis Hiniker, Bill Howe
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 method called Multilevel Embedding Association Test (ML-EAT) is introduced to quantify and interpret intrinsic bias in language technologies. The ML-EAT addresses issues with ambiguity and difficulty in traditional EAT measurement by assessing bias at three levels: differential association, individual effect size, and concept-level associations. A taxonomy of EAT patterns is defined, each associated with a unique EAT-Map visualization for interpreting the ML-EAT results. Empirical analysis of word embeddings, GPT-2 language models, and CLIP demonstrates that EAT patterns reveal component biases, prompting effects, and situations where cosine similarity is unreliable. The proposed method contributes to rendering bias more observable and interpretable, improving transparency in computational investigations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to measure and understand bias in language technology. It’s like trying to figure out what makes people think certain things are funny or serious. They created a special tool called ML-EAT that looks at how words and ideas relate to each other. It shows when certain biases are present, which can help us understand why some things are said or written in certain ways. The researchers tested their tool on different types of language models and found it helped reveal hidden patterns and biases. |
Keywords
» Artificial intelligence » Cosine similarity » Embedding » Gpt » Prompting
