Do LLMs Know Your Neighborhood? Auditing LLM Priors for Neighborhood-Level Mobility Prediction and Structural Alignment

Human mobility is central to urban planning, transportation, public health, and emergency response, yet fine-grained trajectory data are often proprietary, restricted, and privacy-sensitive. Large language models (LLMs) offer a potential alternative by generating plausible mobility traces and predicting individual movement, but their ability to infer aggregate neighborhood-level mobility remains unclear. We evaluate zero-shot LLMs on Census Block Group-level mobility prediction across four U.S. metropolitan areas using anonymized Cuebiq data to construct point-level, trajectory-level, and temporal mobility outcomes, paired with sociodemographic and built-environment predictors. We compare LLM predictions with supervised baselines and introduce a directional alignment analysis to test whether LLM-implied predictor effects agree with empirical OLS and Jonckheere-Terpstra trends. Supervised models achieve 0.580 average accuracy, compared with 0.435 for the best LLM, with spatial extent outcomes showing the strongest predictability but also the largest LLM-baseline gaps. Directional analysis shows that LLMs often rely on coarse, stable predictor-level priors that remain similar across outcomes and cities, including asymmetric treatment of protected-group predictors. Overall, LLMs can partially recover aggregate mobility patterns from urban context, but their predictions should not be treated as structurally grounded without auditing empirical alignment and potential bias.

Who Gets Access? Global Region and Academic Status Bias in AI-Generated Academic Gatekeeping Scenarios

Equitable access to scientific knowledge often depends on informal gatekeeping decisions, particularly when resources such as paywalled articles, datasets, or professional materials such as curriculum vitae (CV) must be shared selectively. We introduce a controlled simulation framework in which large language model (LLM)-based professors must grant access to only one requestor. Across prompts, requesters vary systematically by global region (Global North vs. Global South) and academic seniority (undergraduate student, PhD candidate, postdoctoral researcher, and tenured professor), while all other factors remain constant. Across varying evaluation scenarios, LLMs exhibit contrasting academic status biases, with some prioritizing PhD candidates, while others favor tenured professors. However, when global regions differ, a distinct divergence emerges based on model architecture: while many frontier LLMs systematically favor requesters from the Global South due to pro-equity bias that results from equity-focused safety alignment, open-weight and small models frequently flip this preference to favor the Global North, reflecting the global region bias and unaligned geographic distribution of their baseline pre-training data. Our findings highlight how normative assumptions embedded in model behavior can shape gatekeeping decisions, underscoring the importance of auditing AI systems for fairness and value alignment.