Korean Journal of Buddhist Studies ISSN:1598-0642(Print) 불교학연구

This study proposes expanding the technical verification framework of AI Red Teaming (a core technique for AI safety assessment) into ethical and philosophical dimensions by integrating the concepts of ‘Emptiness’ (空) and the dissolution of the ‘Four Notions’ (四相) from the Diamond Sutra. While Large Language Models (LLMs) have given rise to growing concerns about bias, hallucination, and jailbreaking, current ethics guidelines often remain declarative. In response, this research introduces ‘Right View’ (正見) into technical defense systems to perceive algorithmic biases as substance-less ‘Notions’ (相).
The methodological core lies in mapping the four attachments cautioned against in the Diamond Sutra (the Notions of Self, Person, Being, and Life) to specific error types in modern AI. Beyond simple defect detection, this paper views AI safety as a process of ‘Ethical Alignment’ that uses Right View to identify and correct invisible harms arising from projected human stereotypes. Specifically, it applies the Buddhist concept of Emptiness (空) to complement international standards—such as the OWASP AI Testing Guide and the Guide to Red Teaming Methodology on AI Safety—by addressing the ‘non-fixed existence’ of AI ethics that conventional systems often overlook. This framework deconstructs the ‘discriminative mind’ overlooked by conventional security and explores responsibility through ‘Dependent Origination’ (緣起).
By integrating scientific security with the Middle Way (中道), this study establishes a foundation on which Explainable AI (XAI) can extend beyond technical interpretation to substantive social accountability. Ultimately, the results demonstrates that the wisdom of ‘Non-self’ (無我) can serve as a transformative practical guideline for implementing fairness and reliability. This research extends Buddhist philosophy into the largely unexplored field of AI safety engineering, opening a new horizon where technology and the humanities converge.