From theory to practice: Harmonizing taxonomies of trustworthy AI
The increasing capabilities of AI pose new risks and vulnerabilities for organizations and decision makers. Several trustworthy AI frameworks have been created by U.S. federal agencies and international organizations to outline the principles to which AI systems must adhere for their use to be consi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Health Policy Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590229624000133 |
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| author | Christos A. Makridis Joshua Mueller Theo Tiffany Andrew A. Borkowski John Zachary Gil Alterovitz |
| author_facet | Christos A. Makridis Joshua Mueller Theo Tiffany Andrew A. Borkowski John Zachary Gil Alterovitz |
| author_sort | Christos A. Makridis |
| collection | DOAJ |
| description | The increasing capabilities of AI pose new risks and vulnerabilities for organizations and decision makers. Several trustworthy AI frameworks have been created by U.S. federal agencies and international organizations to outline the principles to which AI systems must adhere for their use to be considered responsible. Different trustworthy AI frameworks reflect the priorities and perspectives of different stakeholders, and there is no consensus on a single framework yet. We evaluate the leading frameworks and provide a holistic perspective on trustworthy AI values, allowing federal agencies to create agency-specific trustworthy AI strategies that account for unique institutional needs and priorities. We apply this approach to the Department of Veterans Affairs, an entity with largest health care system in US. Further, we contextualize our framework from the perspective of the federal government on how to leverage existing trustworthy AI frameworks to develop a set of guiding principles that can provide the foundation for an agency to design, develop, acquire, and use AI systems in a manner that simultaneously fosters trust and confidence and meets the requirements of established laws and regulations. |
| format | Article |
| id | doaj-art-6d28c9de4b164f9ea785d2def05eca1e |
| institution | DOAJ |
| issn | 2590-2296 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Health Policy Open |
| spelling | doaj-art-6d28c9de4b164f9ea785d2def05eca1e2025-08-20T02:49:56ZengElsevierHealth Policy Open2590-22962024-12-01710012810.1016/j.hpopen.2024.100128From theory to practice: Harmonizing taxonomies of trustworthy AIChristos A. Makridis0Joshua Mueller1Theo Tiffany2Andrew A. Borkowski3John Zachary4Gil Alterovitz5Department of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United States; University of Nicosia, Institute for the Future, AGC Towers, 28th October 24, Nicosia 2414, Cyprus; Arizona State University, Business Administration, 300 E Lemon St, Tempe, AZ 85287, United States; Corresponding author.Department of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United StatesDepartment of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United StatesDepartment of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United StatesDepartment of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United StatesDepartment of Veterans Affairs, 810 Vermont Ave NW, Washington DC, 20001, United States; Brigham and Women's Hospital, Harvard Medical School, Center for Biomedical Informatics, Countway Lib, 10 Shattuck St Boston MA 02115, United StatesThe increasing capabilities of AI pose new risks and vulnerabilities for organizations and decision makers. Several trustworthy AI frameworks have been created by U.S. federal agencies and international organizations to outline the principles to which AI systems must adhere for their use to be considered responsible. Different trustworthy AI frameworks reflect the priorities and perspectives of different stakeholders, and there is no consensus on a single framework yet. We evaluate the leading frameworks and provide a holistic perspective on trustworthy AI values, allowing federal agencies to create agency-specific trustworthy AI strategies that account for unique institutional needs and priorities. We apply this approach to the Department of Veterans Affairs, an entity with largest health care system in US. Further, we contextualize our framework from the perspective of the federal government on how to leverage existing trustworthy AI frameworks to develop a set of guiding principles that can provide the foundation for an agency to design, develop, acquire, and use AI systems in a manner that simultaneously fosters trust and confidence and meets the requirements of established laws and regulations.http://www.sciencedirect.com/science/article/pii/S2590229624000133Artificial intelligenceEthicsPolicyGovernmentTrustworthy AI |
| spellingShingle | Christos A. Makridis Joshua Mueller Theo Tiffany Andrew A. Borkowski John Zachary Gil Alterovitz From theory to practice: Harmonizing taxonomies of trustworthy AI Health Policy Open Artificial intelligence Ethics Policy Government Trustworthy AI |
| title | From theory to practice: Harmonizing taxonomies of trustworthy AI |
| title_full | From theory to practice: Harmonizing taxonomies of trustworthy AI |
| title_fullStr | From theory to practice: Harmonizing taxonomies of trustworthy AI |
| title_full_unstemmed | From theory to practice: Harmonizing taxonomies of trustworthy AI |
| title_short | From theory to practice: Harmonizing taxonomies of trustworthy AI |
| title_sort | from theory to practice harmonizing taxonomies of trustworthy ai |
| topic | Artificial intelligence Ethics Policy Government Trustworthy AI |
| url | http://www.sciencedirect.com/science/article/pii/S2590229624000133 |
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