Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
It addresses equity and governance issues in AI adoption for writers and institutions, with incremental contributions across multiple domains.
This dissertation examines the impact of large language models (LLMs) on writing and information ecosystems, showing that AI detectors introduce biases against non-dominant language varieties, LLM adoption is widespread across domains, and LLMs can provide feedback to support researchers with limited access.
Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.