Industrial AI Robustness Card: Evaluating and Monitoring Time Series Models

2025-12-05

AI Summary

Industrial AI deployment faces challenges stemming from ambiguous regulatory robustness requirements and the absence of implementable verification protocols. To address this, we propose the Industrial AI Robustness Card (IARC)—a lightweight, task-agnostic framework that establishes the first standardized robustness evaluation methodology for time-series models. IARC holistically integrates concept drift detection, Bayesian and ensemble-based uncertainty quantification, and physics-informed adversarial stress testing, while systematically mapping all assessments to corresponding compliance clauses of the EU AI Act to ensure regulatory traceability and evidence reproducibility. It supports automated evaluation, continuous monitoring, metadata annotation, and visualization-driven reporting. Validated across three industrial use cases—including biopharmaceutical fermentation soft sensors—IARC significantly improves the efficiency of robustness evidence generation and increases regulatory audit pass rates.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation ready protocols. This paper introduces the Industrial AI Robustness Card (IARC), a lightweight, task agnostic protocol for documenting and evaluating the robustness of AI models on industrial time series. The IARC specifies required fields and an empirical measurement and reporting protocol that combines drift monitoring, uncertainty quantification, and stress tests, and it maps these to relevant EU AI Act obligations. A soft sensor case study on a biopharmaceutical fermentation process illustrates how the IARC supports reproducible robustness evidence and continuous monitoring.

Problem

• Addresses vague robustness requirements in industrial AI regulations
• Introduces a protocol for documenting AI model robustness on time series
• Maps robustness evaluations to EU AI Act compliance obligations

Innovation

• Lightweight protocol for AI model robustness documentation
• Combines drift monitoring, uncertainty quantification, stress tests
• Maps to EU AI Act obligations for compliance