Summary of Popular Llms Amplify Race and Gender Disparities in Human Mobility, by Xinhua Wu and Qi R. Wang
Popular LLMs Amplify Race and Gender Disparities in Human Mobility
by Xinhua Wu, Qi R. Wang
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study investigates the biases of large language models (LLMs) in predicting human mobility based on race and gender. The researchers analyzed three prominent LLMs – GPT-4, Gemini, and Claude – by using prompts that included names with and without demographic details. They found that LLMs frequently reflect and amplify existing societal biases. Specifically, the predictions for minority groups were disproportionately skewed, suggesting that these individuals are less likely to be associated with wealth-related points of interest (POIs). Gender biases were also evident, as female individuals were consistently linked to fewer career-related POIs compared to their male counterparts. The study highlights the importance of understanding LLMs’ biases in predicting human mobility and suggests that they not only mirror but also exacerbate societal stereotypes, particularly in contexts involving race and gender. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how large language models (LLMs) predict where people will go based on their race and gender. The researchers tested three different LLMs by giving them prompts with names that included or didn’t include information about the person’s race and gender. They found that the LLMs often repeated and made worse the biases we see in society today. For example, they predicted that people from minority groups would visit places less often and be less likely to visit places related to wealth. They also found that women were linked to fewer job-related places compared to men. The study shows how important it is to understand how LLMs make predictions about where people will go and suggests that these models can even make biases worse. |
Keywords
» Artificial intelligence » Claude » Gemini » Gpt
