Explainable Benchmarking of LLM API Services: Predictive Quality Assessment, Failure Attribution, and Robustness Analysis Under Adversarial Conditions (Journal of Advanced Artificial Intelligence Research, 2026)
The rapid proliferation of Large Language Model (LLM) application programming interfaces (APIs) as foundational components in socio-technical systems has created an urgent need for rigorous, interpretable, and operationally relevant benchmarking frameworks. Traditional performance evaluations, which emphasize aggregate metrics such as accuracy and latency, fail to capture the nuanced failure modes, predictive degradations, and adversarial vulnerabilities that characterize real-world LLM API deployments. This paper proposes an explainable benchmarking paradigm that integrates predictive quality assessment, systematic failure attribution, and adversarial robustness analysis into a unified evaluation architecture. We ground our approach in structural trade-offs between model fidelity, computational cost, and interpretability, drawing on causal inference and post-hoc explanation methods to attribute API failures to specific model components, input perturbations, or infrastructure bottlenecks. A layered governance framework is introduced to balance transparency, fairness, and sustainability in LLM API provisioning. Through cross-domain analysis of deployment scenarios in healthcare, finance, and content moderation, we illustrate how explainable benchmarking can inform policy decisions, improve system resilience, and foster accountability among API providers. The paper concludes with forward-looking recommendations for integrating predictive quality monitors, failure attribution dashboards, and adversarial testing protocols into continuous deployment pipelines, thereby advancing the reliability and trustworthiness of LLM-based services.
Keywords: explainable AI, LLM API services, benchmarking, adversarial robustness, failure attribution, predictive quality assessment, socio-technical systems, governance