Strengthening Algorithmic Safety Through Responsible and Explainable AI : A Conceptual Framework

Abstract

Purpose : This paper proposed to examine how Responsible AI and Explainable AI jointly contributed to strengthening algorithmic safety. It examined the significant role of ethical governance, transparency, accountability, stakeholder involvement, and explainability to mitigate algorithmic risks and promote trustworthy use of AI decision-making. Design : This review study conceptualized Responsible and Explainable AI (REAI) by integrating the dimensions of Responsible AI (RAI) and Explainable AI (XAI) and further examined how Responsible and Explainable AI (REAI) mechanisms strengthened algorithmic safety. Findings : The paper emphasized that Responsible AI offers an ethical, legal, and governance framework for building safe AI systems, and Explainable AI could help achieve interpretability, traceability, auditability, and contestability. Notably, this timely integration could help mitigate bias, fill accountability gaps, enhance human oversight, enable lifecycle risk management, and safeguard human agency in algorithmic decision-making. Practical Implications : The proposed framework offered significant contributions for AI practitioners, managers, regulators, and policymakers by demonstrating how responsible AI and explainable AI mechanisms could be jointly embedded across the AI lifecycle. It emphasized that continuous risk assessment, human oversight, traceability, stakeholder engagement, and contestability worked as complementary mechanisms for strengthening algorithmic safety. Originality : This paper offered a contribution by conceptualizing algorithmic safety as a process, not just a product, of a socio-technical process. It provided a conceptual framework to connect Responsible AI principles and Explainable AI mechanisms to more human-centered, accountable, safe, transparent AI systems.