The Hidden Racism in Artificial Intelligence Language Models

The use of language models in artificial intelligence has become increasingly common in various applications. However, a recent study conducted by a team of researchers has revealed a troubling aspect of popular language models – covert racism against African American English (AAE) speakers. This finding sheds light on the biases that are embedded in these models, despite efforts to filter overt racism.

Covert racism, as identified by the researchers, involves the use of negative stereotypes that are expressed through assumptions. When presented with text samples in AAE, the language models demonstrated a bias towards using negative adjectives like “dirty,” “lazy,” “stupid,” or “ignorant.” In contrast, when the same questions were phrased in standard English, the responses were more positive. This disparity highlights the presence of covert racism in the language models.

The implications of covert racism in language models are far-reaching. These models are increasingly being used in critical decision-making processes, such as screening job applicants and police reporting. The perpetuation of negative stereotypes through these models can have real-world consequences, reinforcing harmful biases and discrimination against certain groups.

While efforts have been made to filter overt racism in language models, the study suggests that more work is needed to tackle covert racism effectively. Simply adding filters may not be sufficient to eliminate these biases, as they are deeply ingrained in the data used to train the models. It is essential for developers and researchers to recognize and address these biases to ensure fair and unbiased AI systems.

The revelation of covert racism in popular language models is a stark reminder of the challenges in creating unbiased AI systems. As these models continue to play a significant role in various applications, it is crucial to prioritize addressing and eliminating biases to ensure fairness and equality. The research findings underscore the importance of ongoing efforts to remove racism from AI responses and promote inclusivity in artificial intelligence.