The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search
arXiv:2606.06694v1 Announce Type: new Abstract: Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, testing location recommendations across three iterative prompting conditions that progressively add lifestyle preference context and reflect fair h
The rapid integration of LLMs into everyday applications, such as housing search, is making their societal impact increasingly visible and immediate.
This research highlights the critical and immediate impact of AI intermediaries on real-world equity and access, particularly concerning issues like racial bias in housing.
The study demonstrates that LLMs, even without explicit programming, can perpetuate and amplify societal biases like racial steering, necessitating urgent ethical and regulatory considerations.
- · AI ethicists
- · Fair housing advocates
- · Regulatory bodies
- · Unregulated LLM developers
- · Individuals subject to biased algorithms
- · Housing platforms relying solely on LLMs
LLMs mediating access to critical services like housing can inadvertently steer users based on demographic information, leading to biased outcomes.
Increased public and regulatory scrutiny on AI systems to ensure fairness and prevent discrimination in automated decision-making processes across various sectors.
Development of mandated 'fairness by design' principles and auditing standards for AI models, potentially leading to new compliance industries and specialized AI oversight roles.
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Read at arXiv cs.LG