Summary of Laissez-faire Harms: Algorithmic Biases in Generative Language Models, by Evan Shieh et al.
Laissez-Faire Harms: Algorithmic Biases in Generative Language Models
by Evan Shieh, Faye-Marie Vassel, Cassidy Sugimoto, Thema Monroe-White
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 presents a study on the social biases present in generative language models (LMs) when used without explicit identity prompts. It examines how LMs perpetuate harms of omission, subordination, and stereotyping for minoritized individuals with intersectional race, gender, and/or sexual orientation identities. The authors find that these individuals are more likely to encounter LM-generated outputs that portray their identities in a subordinated manner compared to representative or empowering portrayals. The study highlights the prevalence of stereotypes (e.g., perpetual foreigner) that can trigger psychological harms, leading to impaired cognitive performance and increased negative self-perception. The paper concludes with the urgent need to protect consumers from discriminatory harms caused by language models and invest in critical AI education programs tailored towards empowering diverse consumers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how language models can be unfair and hurtful to people who are different. Researchers found that these models often show bad portrayals of people with certain identities, like race or gender. This happens even when we don’t ask the models to focus on those identities. The study shows that this hurts people’s feelings and makes them feel worse about themselves. It’s important for us to make sure language models don’t hurt anyone else. |
