Large language models like ChatGPT may absorb and perpetuate racist biases, as seen in recent research. Despite efforts to mitigate overt racism, the models display covert stereotypes, particularly against African-American English speakers. Feedback training to address biases has been effective for overt racism, but it fails to combat the deeper issue of dialect prejudice. The study highlights the need for better tools to address covert biases as language models continue to grow in size and influence.
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The Impact of Language Models on Bias
Large language models like ChatGPT have been found to absorb biased views, and efforts to mitigate these biases are only addressing overt forms of bias, leaving covert stereotypes to grow stronger.
Research on Bias in AI Models
Recent research tested AI models’ judgments about speakers using African-American English (AAE) and found that the models were more likely to apply negative adjectives and associate less prestigious jobs with AAE speakers compared to Standard American English (SAE) speakers.
Challenges in Mitigating Bias
Efforts to eliminate biased views from models have been effective in combating overt stereotypes but have failed to address covert biases. Researchers have identified a flaw in the current feedback training method used by leading companies like OpenAI, Meta, and Google.
Practical Implications and Future Considerations
The study highlights the need for better tools to combat covert racial bias, especially as AI models continue to increase in size and power. The potential implications of racial bias in AI decision-making, such as in criminal sentencing or hiring processes, call into question the current approach to solving bias in AI models.
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