arXiv AI

Whose fairness? Structural concentration in AI bias research

arXiv:2607. 05574v1 Announce Type: cross Abstract: Artificial intelligence increasingly mediates consequential decisions in healthcare, law, and public services, and the field has responded with an extensive methodology for measuring and mitigating bias.

arXiv AI
Sep 12

Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government

The paper presents a knowledge graph built from Harvard Dataverse’s public data, linking 102,650 datasets to 215,985 nodes and 528,003 edges that include keywords, publications, subjects, journals, and locations. About 43,991 datasets contain geospatial metadata, and 7,654 are identified as policy‑relevant, with elections and legislatures forming the largest cluster. The authors highlight the challenge of place resolution—disconnected nodes representing the same location—and propose the graph as a testbed for AI‑driven metadata enrichment and entity resolution, noting a bias toward American city‑level data.

By Danny EBanks, Devika Jain
arXiv AI
Sep 10

Mapping the Emerging Social Science of Large Language Models

The paper maps the nascent social‑science literature on large language models (LLMs) by analysing 198 curated papers and 47,719 field‑scale papers. It identifies three main domains—LLM as Social Minds, LLM Societies, and LLM‑Human Interactions—each containing 13 subcategories such as reasoning, bias, collective intelligence, and trust. The taxonomy is validated through clustering stability, author classification agreement, and topic mapping, revealing differing prominence across conference and journal venues.

By Yi Yang, Xiao Jia, Zeyun Dong, Chenzhang Wang, Zhanzhan Zhao
arXiv AI
2d ago

Science Utopia? Closed-Loop LLM Simulation of Academic Research Ecosystems

The paper introduces SciUtopia, a closed‑loop large‑language‑model simulation framework that models the evolving academic research ecosystem, including research direction, collaboration, publication, peer review, funding, and researcher attrition. Running 61 simulation worlds, the system simulates over 40,000 researchers and 1.2 million LLM‑generated peer reviews, revealing that rejection‑driven resubmission increases reviewer burden, cautious exploration balances citation impact with career success, and resource inequality can arise without early‑funding advantage.

By Yiqiao Jin, Yiyang Wang, Lucheng Fu, Bing He, Siheng Xiong, Yijia Xiao, B. Aditya Prakash, Josiah Hester, Srijan Kumar, James Evans, Jindong Wang