Artificial Intelligence (AI) has become an integral part of our lives, with Language Model Models (LLMs) like ChatGPT gaining popularity. However, a recent study has shed light on a concerning issue – covert racism in popular LLMs.
A team of AI researchers from the Allen Institute for AI, Stanford University, and the University of Chicago discovered that LLMs exhibit covert racism against individuals who speak African American English (AAE). This covert racism, unlike overt racism, is harder to detect and prevent. The team trained multiple LLMs on AAE text samples and observed the responses to questions about the user.
Findings
The research revealed that when prompted with questions in AAE, all the LLMs provided negative adjectives like “dirty,” “lazy,” and “stupid” to describe the user. However, when the same questions were phrased in standard English, the responses were more positive. This brings to light the presence of negative stereotypes and biases in LLMs, which have serious implications.
The implications of covert racism in AI models are concerning, especially considering the growing use of LLMs for tasks like screening job applicants and police reporting. The study highlights the urgent need to address and eliminate racism from AI systems to ensure fairness and equity.
One of the challenges identified in the study is the difficulty in detecting and preventing covert racism in LLMs. While measures have been taken to address overt racism, such as implementing filters, covert racism remains an ongoing issue. Negative stereotypes and assumptions in the text can lead to biased responses, perpetuating harmful narratives.
The study emphasizes the importance of addressing covert racism in AI models. As LLMs continue to evolve and be integrated into various applications, it is crucial to ensure that they are free from bias and discrimination. More research and efforts are needed to eliminate racism from LLM responses and promote fairness in AI technologies.
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