About

Dr. Michael L. Best is the Executive Director of the Institute for People and Technology (IPaT) and a Professor with the Sam Nunn School of International Affairs and the School of Interactive Computing at Georgia Institute of Technology. He directs the Technologies and International Development Lab. He holds a Ph.D. from MIT and has served as director of Media Lab Asia in India and as head of the eDevelopment group at the MIT Media Lab. His research focuses on computing and global development, integrating technology with international development initiatives.

Research topics

• Computer Science
• Political Science
• Sociology
• Computer Security
• Artificial Intelligence
• Data science
• Public relations
• Environmental science
• Biology
• World Wide Web
• Knowledge management
• Ecology
• Engineering
• Environmental resource management

Selected publications

• Tackling Global AI-Driven Hiring Bias: A Literature Review from an HCI Perspective

  Lecture notes in computer science · 2026-01-01

  book-chapterSenior author
  PublisherDOI

• Cash Transfers and Productive Inclusion: Evidence from Bolsa Familia

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Improving state effectiveness in environmental risk mitigation: Experimental evidence from Bangladesh

  AEA Randomized Controlled Trials · 2025-10-12

  dataset1st authorCorresponding
  PublisherDOI

• A Survey of NLP Progress in Sino-Tibetan Low-Resource Languages

  2025-01-01 · 1 citations

  articleOpen accessSenior author

  Shuheng Liu, Michael Best. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

  PublisherOA PDFDOI

• Africa Health Check: Probing Cultural Bias in Medical LLMs

  2025-01-01

  articleOpen accessSenior author

  Large language models (LLMs) are increasingly deployed in global healthcare, yet their outputs often reflect Western-centric training data and omit indigenous medical systems and region-specific treatments.This study investigates cultural bias in instruction-tuned medical LLMs using a curated dataset of African traditional herbal medicine.We evaluate model behavior across two complementary tasks, namely, multiple-choice questions and fill-in-the-blank completions, designed to capture both treatment preferences and responsiveness to cultural context.To quantify outcome preferences and prompt influences, we apply two complementary metrics: Cultural Bias Score (CBS) and Cultural Bias Attribution (CBA).Our results show that while prompt adaptation can reduce inherent bias and enhance cultural alignment, models vary in how responsive they are to contextual guidance.Persistent default to allopathic 1 (Western) treatments in zero-shot scenarios suggest that many biases remain embedded in model training.These findings underscore the need for culturally informed evaluation strategies to guide the development of AI systems that equitably serve diverse global health contexts.By releasing our dataset and providing a dual-metric evaluation approach, we offer practical tools for developing more culturally aware and clinically grounded AI systems for healthcare settings in the Global South.

  PublisherOA PDFDOI

• Improving state effectiveness in environmental risk mitigation: Experimental evidence from Bangladesh

  AEA Randomized Controlled Trials · 2025-10-12

  dataset1st authorCorresponding
  PublisherDOI

• Caring Within Platform Capitalism: Community Health Workers’ Perspectives on Social Media for Health Promotion and Advocacy

  2025-07-18 · 2 citations

  articleOpen accessSenior author
  PublisherDOI

• AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset

  2025-01-01 · 5 citations

  articleOpen access

  Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

  PublisherOA PDFDOI

• Community-Driven Data Practices for Advancing Ethical and Equitable AI in Low-Resource Language Contexts

  2025-07-18

  articleSenior author
  PublisherDOI

• Greener on the Other Side: Inequity and Tax Compliance

  SSRN Electronic Journal · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Christian E. Grue

  University of Washington

  24 shared

• Hartwell H. Welsh

  Pacific Southwest Research Station

  21 shared

• Erin Muths

  16 shared

• Marc P. Hayes

  Washington Department of Fish and Wildlife

  16 shared

• Ernest J. Wilson

  13 shared

• Robert T. Mason

  Oregon State University

  12 shared

• Andrew J. Kroll

  Weyerhaeuser (United States)

  12 shared

• David S. Pilliod

  United States Geological Survey

  10 shared