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.