RAISEF: A Driver-Based Framework for Responsible AI Integrating Academic and Practical Perspectives

Authors

  • Richard Khan Golden Gate University

Keywords:

Artificial Intelligence, Responsible AI, Ethical Safeguards, Operational Integrity, Societal Empowerment

Abstract

AI is rapidly proliferating in many industries, presenting unprecedented opportunities and challenges impacting humanity across ethical, regulatory, and societal dimensions.  This study introduces the Responsible AI System Evolution Framework or RAISEF. It is a novel lifecycle-based approach that helps stakeholders navigate evolving challenges, bridging theoretical constructs with practical applications. It organizes 15 interdependent, Responsible AI drivers into three pillars: ethical safeguards, operational integrity, and societal empowerment. RAISEF systematically addresses many inter-driver tensions such as privacy versus explainability and fairness versus robustness. Additionally, it promotes synergies for cohesive, Responsible AI implementation. Furthermore, unlike existing models, RAISEF integrates cross-disciplinary insights from ethics, governance, sociology, and systems thinking. It advances the theoretical discourse while offering actionable methodologies and toolkits tailored to diverse cultural and environmental contexts. The paper, through mainly hypothetical and empirical scenarios, illustrates RAISEF's adaptability to emerging challenges, including autonomous systems, generative AI, and global policy variations. It unites theory and practice. RAISEF provides a comprehensive, globally adaptable framework for academics, policymakers, and practitioners to foster the development of ethical, sustainable, and trustworthy AI systems.

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Published

2025-08-11