Evaluatology’s perspective on AI evaluation in critical scenarios: From tail quality to landscape

Tail Quality, as a metric for evaluating AI inference performance in critical scenarios, reveals the extreme behaviors of AI inference systems in real-world applications, offering significant practical value. However, its adoption has been limited due to the lack of systematic theoretical support. To address this issue, this paper analyzes AI inference system evaluation activities from the perspective of Evaluatology, bridging the gap between theory and practice. Specifically, we begin by constructing a rigorous, consistent, and comprehensive evaluation system for AI inference systems, with a focus on defining the evaluation subject and evaluation conditions. We then refine the Quality@Time-Threshold (Q@T) statistical evaluation framework by formalizing these components, thereby enhancing its theoretical rigor and applicability. By integrating the principles of Evaluatology, we extend Q@T to incorporate stakeholder considerations, ensuring its adaptability to varying time tolerance. Through refining the Q@T evaluation framework and embedding it within Evaluatology, we provide a robust theoretical foundation that enhances the accuracy and reliability of AI system evaluations, making the approach both scientifically rigorous and practically reliable. Experimental results further validate the effectiveness of this refined framework, confirming its scientific rigor and practical applicability. The theoretical analysis presented in this paper provides valuable guidance for researchers aiming to apply Evaluatology in practice. • Theoretical Foundation for Tail Quality: This paper bridges the gap between theory and practice by applying Evaluatology to the evaluation of AI inference systems, providing a solid theoretical framework for Tail Quality, an insight that reveals extreme behaviors in critical AI applications. • Refinement and Adaptation of Q@T: We refine the Quality@Time-Threshold (Q@T) evaluation framework by formalizing the evaluation subject and conditions, improving its theoretical rigor and adaptability to various application scenarios. • Incorporating Stakeholder Considerations: The paper extends Q@T by integrating stakeholder-driven time tolerance considerations providing a more comprehensive evaluation of AI system reliability in real-world contexts.