Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges

These files are supplementary files for the “Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges” study, prepared for the Hawaii International Conference on System Sciences (HICSS 2026) File "Data_AI_challenges.docx" presents the list of data-AI challenges surrounding AI adoption in public sector as identified through the SLR, with SLR results available in the file "Data_AI_challenges_SLR". File "Responsible_AI_Adoption_AI_Challenges_public_sector_AN.pdf" a pre-print version of the manuscript prepared for the Hawaii International Conference on System Sciences (HICSS 2026). It is posted here for your personal use. Not for redistribution. Digital Object Identifier (DOI) and link to the article will be added once they are assigned. Abstract: Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. This study addresses this gap by developing a taxonomy of data-related challenges to responsible AI adoption in government. Based on a systematic review of 43 studies and 21 expert evaluations, the taxonomy identifies 13 key challenges across technological, organizational, and environmental dimensions, including poor data quality, limited AI-ready infrastructure, weak governance, misalignment in human-AI decision-making, economic and environmental sustainability concerns. Annotated with institutional pressures, the taxonomy serves as a diagnostic tool to surface “symptoms” of high-risk AI deployment and guides policymakers in building the institutional and data governance conditions necessary for responsible AI adoption. Please cite this paper as: Nikiforova, A., Lnenicka, M., Melin, U., Valle-Cruz, D., Gill, A., Casiano Flores, C., Sirait, E., Luterek, M., Dreyling, R. M., and Tesarova, B. (2025). Responsible AI Adoption in the Public Sector: A Data Centric Taxonomy of AI Adoption Challenges. In Proceedings of the 59th Hawaii International Conference on System Sciences