TY - GEN
T1 - System Card+
T2 - 1st International Conference on Information Technology and Artificial Intelligence, ITAI 2025
AU - Tibebu, Haileleol
AU - Kakadiaris, Ioannis A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - Artificial Intelligence’s increasing complexity and societal impact demand a robust performance, fairness, inclusivity, and ethical and legal assessment framework. Current approaches often lack the adaptability and context-specific focus to establish responsible AI use. This paper introduces a theoretical framework for AI-based Decision Support Systems, a modular approach designed to address these limitations and bridge the gap between complex AI regulations and their practical implementation. The methodology offers a five-layer benchmarking system. The first layer verifies the performance accountability of the AI system, while the second layer evaluates its fairness. The third layer addresses inclusivity, the fourth examines ethical competency, and the fifth assesses legal compliance. Each layer evaluates AI-based Decision Support Systems throughout the AI life cycle’s development, assessment, mitigation, and assurance stages, including data, models, code, and the system. This decomposed, step-by-step approach simplifies compliance efforts, aligning with major international regulations such as the EU AI Act, the NIST AI framework, and the Algorithmic Accountability Act. This work contributes to developing a universal standard for responsible AI practices.
AB - Artificial Intelligence’s increasing complexity and societal impact demand a robust performance, fairness, inclusivity, and ethical and legal assessment framework. Current approaches often lack the adaptability and context-specific focus to establish responsible AI use. This paper introduces a theoretical framework for AI-based Decision Support Systems, a modular approach designed to address these limitations and bridge the gap between complex AI regulations and their practical implementation. The methodology offers a five-layer benchmarking system. The first layer verifies the performance accountability of the AI system, while the second layer evaluates its fairness. The third layer addresses inclusivity, the fourth examines ethical competency, and the fifth assesses legal compliance. Each layer evaluates AI-based Decision Support Systems throughout the AI life cycle’s development, assessment, mitigation, and assurance stages, including data, models, code, and the system. This decomposed, step-by-step approach simplifies compliance efforts, aligning with major international regulations such as the EU AI Act, the NIST AI framework, and the Algorithmic Accountability Act. This work contributes to developing a universal standard for responsible AI practices.
KW - AI accountability
KW - Fairness
KW - Responsible AI
KW - System-card+
UR - https://www.scopus.com/pages/publications/105023313465
UR - https://www.scopus.com/inward/citedby.url?scp=105023313465&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-8687-2_30
DO - 10.1007/978-981-96-8687-2_30
M3 - Conference contribution
AN - SCOPUS:105023313465
SN - 9789819686865
T3 - Lecture Notes in Networks and Systems
SP - 413
EP - 436
BT - Proceedings of International Conference on Information Technology and Artificial Intelligence - ITAI 2025
A2 - Kumar, Sandeep
A2 - Bye, Robin T.
A2 - Prasad, Mukesh
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 24 January 2025 through 25 January 2025
ER -