In a new AI research paper, a team of researchers from Stanford Law School has investigated biases present in state-of-the-art large language models (LLMs), including GPT-4, focusing particularly on disparities related to race and gender. It highlights the potential harm caused by biases encoded in these models, especially when providing advice across various scenarios, such as car purchase negotiations or election outcome predictions. The paper aims to shed light on the systemic nature of biases in LLMs and propose methods to mitigate their harmful effects on marginalized communities.
Current methods struggle to address biases in LLMs, particularly the ones related to race and gender. While some efforts have been made to mitigate biases by avoiding explicit references to sensitive attributes, such as race or gender, researchers found that biases can still manifest through features strongly correlated with these attributes, such as names. To address this issue, the researchers propose an audit design that directly prompts LLMs with scenarios involving named individuals, varying the names to assess biases across racial and gender associations.
The proposed audit design involves structuring scenarios across multiple domains where LLMs provide advice to users, such as purchasing decisions or election predictions. By varying the names associated with individuals in these scenarios, the researchers aim to identify and quantify biases in the model’s responses. They employ three levels of contextual detail in the prompts: low context, high context, and numeric context, to evaluate the impact of additional information on bias mitigation. Through this approach, the study gathers quantitative data on disparities across different racial and gender associations, revealing systematic biases in LLM outputs.
The data indicate that names highly linked with ethnic minorities and women had consistently negative consequences across a variety of contexts. Providing qualitative context has mixed effects on biases, whereas a numerical anchor effectively eliminates differences in most circumstances. The paper also investigates intersectional biases, demonstrating that black women are especially disadvantaged. It further examines biases among different LLMs, revealing comparable trends across models, and demonstrating the prevalence of biases in cutting-edge language models.
In conclusion, the paper highlights the pervasive biases present in state-of-the-art LLMs, particularly concerning race and gender disparities. The proposed audit design provides a method to identify and quantify these biases, emphasizing the importance of conducting audits at LLMs’ deployment and implementation stages. While qualitative context may not consistently mitigate biases, numeric anchors offer a promising strategy for bias reduction.
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