Summary of Bias in Text Embedding Models, by Vasyl Rakivnenko et al.
Bias in Text Embedding Models
by Vasyl Rakivnenko, Nestor Maslej, Jessica Cervi, Volodymyr Zhukov
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 paper explores the potential for bias in popular text embedding models, specifically examining their associations with gendered terms and professions. It reveals that these models are prone to gendered biases, but with varying degrees and directions depending on the model and prompts used. The analysis shows that certain models more strongly associate feminine occupations with female identifiers and masculine occupations with male identifiers, while others exhibit different patterns of bias. The study highlights the need for businesses using text embedding technology to be aware of these specific biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Text embedding is a popular AI method that can be biased towards gender, according to this research. It looks at how certain professions are linked to female or male words in many models. The results show that different models make different connections between jobs and genders, but all have some level of bias. This means companies using these models should think about the potential biases they might introduce. |
Keywords
» Artificial intelligence » Embedding
