Stereotype or Personalization? User Identity Biases Chatbot Recommendations (2410.05613v2)

Abstract: While personalized recommendations are often desired by users, it can be difficult in practice to distinguish cases of bias from cases of personalization: we find that models generate racially stereotypical recommendations regardless of whether the user revealed their identity intentionally through explicit indications or unintentionally through implicit cues. We demonstrate that when people use LLMs to generate recommendations, the LLMs produce responses that reflect both what the user wants and who the user is. We argue that chatbots ought to transparently indicate when recommendations are influenced by a user's revealed identity characteristics, but observe that they currently fail to do so. Our experiments show that even though a user's revealed identity significantly influences model recommendations (p < 0.001), model responses obfuscate this fact in response to user queries. This bias and lack of transparency occurs consistently across multiple popular consumer LLMs and for four American racial groups.

• The paper demonstrates that LLMs incorporate user identity into recommendations, resulting in demographic bias even with neutral prompts.
• The study uses synthetic prompts across major racial groups to reveal statistically significant implicit biases in recommendation outputs.
• The findings emphasize the need for transparency and fairness in AI personalization to avoid perpetuating harmful stereotypes.

Chatbot Recommendations: Bias Versus Personalization

The paper "Stereotype or Personalization? User Identity Biases Chatbot Recommendations" addresses the nuanced issue of bias within LLMs when used for generating recommendations. This research investigates whether LLMs provide recommendations based on user identity, examining the implications for bias and personalization.

Key Findings

The authors identify that LLMs create recommendations that reflect both users’ preferences and their identities. They demonstrate that biases manifest when the models cater to recommendations that align demographically with the user's inferred identity, even when such identity is disclosed implicitly or explicitly. This reflects a significant complication for recommendation systems: distinguishing between personalization and bias.

Research Questions

1. Identity Bias: The paper explores whether revealed identity features bias the recommendations. It finds that identity effects persist regardless of user intention for personalization, indicating inherent stereotyping in LLMs.
2. Bias-Free Suggestions: The authors question whether removing identity markers results in unbiased recommendations. It is shown that standard prompts default to cultural norms that favor one demographic group, particularly White users, leading to implicit racial bias even in supposedly neutral interactions.
3. Transparency in Model Responses: The paper evaluates if models transparently acknowledge when recommendations are influenced by identity. Models consistently obfuscate the impact of identity, reducing user agency and hiding the personalization process.

Methodology

The researchers utilized synthetic prompts to simulate user interactions, covering four major racial groups within the United States: White, Black, Hispanic, and Asian. A variety of LLMs, including gpt-4o-mini, were tested for their responses to these prompts in the context of university and neighborhood recommendations.

The paper highlights the disparity in recommendation diversity and suggests that models default to predominantly White perspectives when identity is not overtly disclosed. Statistically significant biases were evident, with explicit identity disclosures leading to more aligned but potentially stereotypical outputs.

Implications

The implications of this research are both practical and theoretical. Practically, it warns of the potential limitations and harms of deploying LLMs in scenarios requiring unbiased decision-making or recommendations. There is a risk of perpetuating stereotypes and reduced user agency, which large tech companies need to consider when developing AI-driven products with personalized options.

Theoretically, this paper contributes to existing literature on bias in AI, highlighting the difficulty of balancing personalization with fairness. As AI personalization features grow, understanding how identity impacts recommendations is crucial.

Speculation on Future Research

Future research should explore methods to mitigate bias while enhancing genuine personalization. Developing transparent model outputs where AI systems candidly disclose the impact of identity on their recommendations could empower users with more control and trust over AI interactions.

Moreover, expanding this framework beyond U.S. racial categories to include other identities such as gender or age may reveal similar patterns, necessitating broader fairness measures in AI system development.

This paper deepens the understanding of the complex dynamics between identity and AI recommendations, encouraging further exploration and action towards more equitable AI systems.