Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges
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arXiv
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| Format: | Preprint |
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2025
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| author | Nikiforova, Anastasija Lnenicka, Martin Melin, Ulf Valle-Cruz, David Gill, Asif Flores, Cesar Casiano Sirait, Emyana Luterek, Mariusz Dreyling, Richard Michael Tesarova, Barbora |
| author_facet | Nikiforova, Anastasija Lnenicka, Martin Melin, Ulf Valle-Cruz, David Gill, Asif Flores, Cesar Casiano Sirait, Emyana Luterek, Mariusz Dreyling, Richard Michael Tesarova, Barbora |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09634 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges Nikiforova, Anastasija Lnenicka, Martin Melin, Ulf Valle-Cruz, David Gill, Asif Flores, Cesar Casiano Sirait, Emyana Luterek, Mariusz Dreyling, Richard Michael Tesarova, Barbora Computers and Society Artificial Intelligence 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. |
| title | Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2510.09634 |