arXiv subjects

Aurora Zhang

Publications and source records attributed to Aurora Zhang.

2 recordsLinked to original sources

arXiv2025

Quantitative Insights into Large Language Model Usage and Trust in Academia: An Empirical Study

Large Language Models (LLMs) are transforming writing, reading, teaching, and knowledge retrieval in many academic fields. However, concerns regarding their misuse and erroneous outputs have led to varying degrees of trust in LLMs within academic communities. In response, various academic organizations have proposed and adopted policies regulating their usage. However, these policies are not based on substantial quantitative evidence because there is no data about use patterns and user opinion. Consequently, there is a pressing need to accurately quantify their usage, user trust in outputs, and concerns about key issues to prioritize in deployment. This study addresses these gaps through a quantitative user study of LLM usage and trust in academic research and education. Specifically, our study surveyed 125 individuals at a private R1 research university regarding their usage of LLMs, their trust in LLM outputs, and key issues to prioritize for robust usage in academia. Our findings reveal: (1) widespread adoption of LLMs, with 75% of respondents actively using them; (2) a significant positive correlation between trust and adoption, as well as between engagement and trust; and (3) that fact-checking is the most critical concern. These findings suggest a need for policies that address pervasive usage, prioritize fact-checking mechanisms, and accurately calibrate user trust levels as they engage with these models. These strategies can help balance innovation with accountability and help integrate LLMs into the academic environment effectively and reliably.

cs.CY↗

arXiv2024

Structural Interventions and the Dynamics of Inequality

Recent conversations in the algorithmic fairness literature have raised several concerns with standard conceptions of fairness. First, constraining predictive algorithms to satisfy fairness benchmarks may lead to non-optimal outcomes for disadvantaged groups. Second, technical interventions are often ineffective by themselves, especially when divorced from an understanding of structural processes that generate social inequality. Inspired by both these critiques, we construct a common decision-making model, using mortgage loans as a running example. We show that under some conditions, any choice of decision threshold will inevitably perpetuate existing disparities in financial stability unless one deviates from the Pareto optimal policy. Then, we model the effects of three different types of interventions. We show how different interventions are recommended depending upon the difficulty of enacting structural change upon external parameters and depending upon the policymaker's preferences for equity or efficiency. Counterintuitively, we demonstrate that preferences for efficiency over equity may lead to recommendations for interventions that target the under-resourced group. Finally, we simulate the effects of interventions on a dataset that combines HMDA and Fannie Mae loan data. This research highlights the ways that structural inequality can be perpetuated by seemingly unbiased decision mechanisms, and it shows that in many situations, technical solutions must be paired with external, context-aware interventions to enact social change.

cs.CY↗