The paper presents a case study of Tribal land descriptions in which AI models produced text that appears fluent and confident but may be thematically skewed toward biased cultural framing. The authors observed this problem while drafting the 2026 TPI Tribal Lands Broadband Report. Prompted without detailed guidance, models produced outputs organized around heritage, spirituality, and tradition rather than the geographic and infrastructure characteristics appropriate for broadband planning. The paper argues that this output poses a practical risk for any domain in which AI-generated text may be applied without careful review and demonstrates that structured prompting can redirect outputs toward task relevance. The problem, while real, is largely addressable at the prompt level.
Sarah Oh Lam is a Senior Fellow at the Technology Policy Institute. Oh completed her PhD in Economics from George Mason University, and holds a JD from GMU and a BS in Management Science and Engineering from Stanford University. She was previously the Operations and Research Director for the Information Economy Project at George Mason School of Law. She has also presented research at the 39th Telecommunications Policy Research Conference and has co-authored work published in the Northwestern Journal of Technology & Intellectual Property among other research projects. Her research interests include law and economics, regulatory analysis, and technology policy.
