Fairness has been touted as one of the most important issues for responsible AI as sophisticated AI-powered systems increasingly impact human lives. At the same time, access to information is essential in today’s knowledge economy and fundamental to American democracy. However, certain groups of the population might be excluded or lack access to participate fully in public discourse/economy because their cultural background presents obstacles to accessing or comprehending uniformly disseminated information without consideration of cultural relevance. The current study is the first study of a large research project on AI fairness and information access designed to explore how different minority groups perceive their information access needs and how cultural factors might shape their information access experiences. Data was collected through 49 comprehensive interviews conducted with a variety of ethnic groups. Thematic analysis technique and NVivo 12 plus were used to analyze data. Findings revealed that while ethnic users can identify several challenges and issues regarding their information access, they either do not realize or cannot explicitly articulate how their ethnic backgrounds or cultural factors would shape their information access experiences.


AI, cultural sensitivity, information access, ethnic groups, phenomenology, in-depth interview

Author Bio(s)

Dr. Huan Chen is an associate professor in the Department of Advertising at the University of Florida. Her research interests include new media and advertising, international and cross-cultural advertising, and social media and health communication. In the past two years, Chen has developed a research line focusing on AI and communication. Chen has published more than 70 articles in leading refereed journals, such as the Journal of Advertising, International Journal of Advertising, Journal of Business Research, and Health Communication, among others. Her research articles were selected as one of the 2019 most influential articles of the Journal of Advertising and one of the most-read articles of the Journal of Current Issues and Research in Advertising. She serves on the editorial board of the Journal of Advertising, International Journal of Advertising, Journal of Advertising Research, and Journal of Ethnography and Qualitative Research, and has served as a reviewer for numerous journals and conferences. Chen has received the top paper and research awards and recognition from national and international communication associations and conferences. She was awarded the Research Fellowship of the American Academy of Advertising in 2017, 2020, and 2022. She was the recipient of the Annual McGraw Hill Distinguished Scholar Award of the EQRC conference in 2019. Please direct correspondence to huanchen@jou.ufl.edu.

Dr. Sylvia Chan-Olmsted is the Director of Media Consumer Research in the College of Journalism and Communications at the University of Florida. A Professor of Media Management and Consumer, Dr. Chan-Olmsted’s research expertise includes emerging media consumption, brand and media engagement, brand trust, and AI applications in media and marketing communications. A prolific scholar, her current studies involve media brand trust measurement, machine learning applications for messaging purposes, and sustainability issues in media management contexts. Dr. Chan-Olmsted has conducted consumer research for Meta, Google, Adidas, U.S. National Association of Broadcasters, the Cable Center, Nielsen, Huffington Post (Germany), Bertelsmann (Gruner + Jahr), many global industry partners, as well as the NSF. Recipient of over 50 national and international research awards and author of hundreds of publications, Dr. Chan-Olmsted currently holds the Al and Effie Flanagan Professorship at the University of Florida.

Dr. My T. Thai is a UF Research Foundation Professor of Computer & Information Sciences & Engineering and Associate Director of Nelms Institute for the Connected World at the University of Florida. Dr. Thai has extensive expertise in billion-scale data mining, machine learning, and optimization, especially for complex graph data with applications to blockchain, social media, critical networking infrastructure, cybersecurity, and healthcare. The results of her work have led to seven books and 220+ publications in leading academic journals and conferences, including 2014 IEEE MSN Best Paper Award, 2017 IEEE ICDM Best Papers Award, and 2018 IEEE/ACM ASONAM Best Paper Runner Up. In 2009, Dr. Thai was awarded the Young Investigator (YIP) from the Defense Threat Reduction Agency (DTRA) and in 2010, she won the NSF CAREER Award. She was also awarded the UF Research Foundation Professorship in 2016. Dr. Thai is a Fellow of the IEEE. Dr. Thai has engaged in many professional activities, including being TPC-chairs of many IEEE international conferences and on the editorial board of several journals. She is presently the Editor-in-Chief of Journal of Combinatorial Optimization (JOCO) journal. She is a book series editor of Springer Optimization and its Application.


This research project is sponsored by UF Research Artificial Intelligence (AI) Research Catalyst Fund.

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