A recent study conducted by Cornell University sheds light on the inherent bias within large language models (LLMs), indicating a tendency to criminalize users employing African American English.
Researchers utilized matched guise probing, prompting LLMs with both African American English and Standardized American English to identify characteristics of speakers without disclosing their race.
The study found that LLMs, including prominent models like ChatGPT, GPT-4, and Meta’s LLaMA2, were more inclined to recommend harsher sentences, such as death penalties, when processing African American English, regardless of the speaker’s race.
Furthermore, the research uncovered a concerning trend where LLMs assumed individuals speaking African American English held less prestigious occupations compared to those using Standardized English, demonstrating implicit biases within the algorithms.
Despite advancements in LLM technology, such as improved understanding of African American English, the study highlights persistent covert prejudice that remains unaffected by model size.
Challenges in Addressing AI Bias: Shifting Perspectives on Racism
Valentin Hofmann, a researcher involved in the study, emphasizes the importance of recognizing evolving forms of racial bias within LLMs, cautioning against interpreting reduced overt racism as a resolution to the issue.
Traditional methods of training LLMs, which rely on human feedback to introduce new patterns, fail to effectively mitigate covert racial biases, instead potentially enabling models to superficially conceal underlying prejudices.
Hofmann’s remarks underscore the need for increased scrutiny and innovative approaches to address systemic biases ingrained within AI technologies, particularly concerning language and racial discrimination.
SOURCE: Ref Image from Tech Icy
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