Summary of What’s in a Name? Auditing Large Language Models For Race and Gender Bias, by Alejandro Salinas et al.
What’s in a Name? Auditing Large Language Models for Race and Gender Bias
by Alejandro Salinas, Amit Haim, Julian Nyarko
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates biases in large language models like GPT-4 by prompting them with advice scenarios involving named individuals, such as car purchase negotiations or election predictions. The study finds that these models systematically disadvantage names associated with racial minorities and women, with Black women receiving the least advantageous outcomes. Biases are consistent across 42 prompt templates and multiple models, indicating a systemic issue rather than isolated incidents. While numerical anchors in the prompt can counteract biases, qualitative details have inconsistent effects and may even increase disparities. The findings highlight the importance of conducting audits at the point of LLM deployment to mitigate harm against marginalized communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models like GPT-4 are very smart computers that can understand and generate human-like text. But do they always make fair decisions? This paper investigates biases in these models by testing how they give advice when given scenarios involving named individuals, like a person buying a car or predicting election outcomes. The results show that the models often unfairly disadvantage names associated with racial minorities and women. Black women get the worst treatment. The study finds this unfairness happens consistently across many different prompts and models, not just one or two mistakes. To fix this problem, providing numerical anchors in the prompt can help, but adding extra details can sometimes make things worse. This highlights how important it is to check these language models before using them to make sure they don’t hurt marginalized communities. |
Keywords
* Artificial intelligence * Gpt * Prompt * Prompting
