A Computationally Efficient Learning of Artificial Intelligence System Reliability Considering Error Propagation
arXiv:2603.18201v1 Announce Type: new Abstract: Artificial Intelligence (AI) systems are increasingly prominent in emerging smart cities, yet their reliability remains a critical concern. These systems typically operate through a sequence of interconnected functional stages, where upstream errors may propagate to downstream stages, ultimately affecting overall system reliability. Quantifying such error propagation is essential for accurate modeling of AI system reliability. However, this task is challenging due to: i) data availability: real-world AI system reliability data are often scarce and constrained by privacy concerns; ii) model validity: recurring error events across sequential stages are interdependent, violating the independence assumptions of statistical inference; and iii) computational complexity: AI systems process large volumes of high-speed data, resulting in frequent and complex recurrent error events that are difficult to track and analyze. To address these challeng
arXiv:2603.18201v1 Announce Type: new Abstract: Artificial Intelligence (AI) systems are increasingly prominent in emerging smart cities, yet their reliability remains a critical concern. These systems typically operate through a sequence of interconnected functional stages, where upstream errors may propagate to downstream stages, ultimately affecting overall system reliability. Quantifying such error propagation is essential for accurate modeling of AI system reliability. However, this task is challenging due to: i) data availability: real-world AI system reliability data are often scarce and constrained by privacy concerns; ii) model validity: recurring error events across sequential stages are interdependent, violating the independence assumptions of statistical inference; and iii) computational complexity: AI systems process large volumes of high-speed data, resulting in frequent and complex recurrent error events that are difficult to track and analyze. To address these challenges, this paper leverages a physics-based autonomous vehicle simulation platform with a justifiable error injector to generate high-quality data for AI system reliability analysis. Building on this data, a new reliability modeling framework is developed to explicitly characterize error propagation across stages. Model parameters are estimated using a computationally efficient, theoretically guaranteed composite likelihood expectation - maximization algorithm. Its application to the reliability modeling for autonomous vehicle perception systems demonstrates its predictive accuracy and computational efficiency.
Executive Summary
This article proposes a computationally efficient learning framework for assessing the reliability of Artificial Intelligence (AI) systems, particularly in the context of autonomous vehicle perception systems. The framework leverages a physics-based simulation platform to generate high-quality data, which is then analyzed using a novel composite likelihood expectation-maximization algorithm. The approach addresses the challenges of data scarcity, model validity, and computational complexity associated with AI system reliability analysis. The framework demonstrates predictive accuracy and computational efficiency, providing a valuable contribution to the field of AI reliability modeling.
Key Points
- ▸ The article addresses the critical concern of AI system reliability in emerging smart cities.
- ▸ A physics-based autonomous vehicle simulation platform is leveraged to generate high-quality data for AI system reliability analysis.
- ▸ A new reliability modeling framework is developed to explicitly characterize error propagation across stages in AI systems.
Merits
Strength in Addressing Data Scarcity
The article proposes a novel approach to addressing the scarcity of real-world AI system reliability data, which is a critical limitation in the field.
Computational Efficiency
The composite likelihood expectation-maximization algorithm is computationally efficient, making it a valuable contribution to the field of AI reliability modeling.
Demerits
Limited Generalizability
The article focuses on autonomous vehicle perception systems, and the generalizability of the proposed framework to other AI systems and domains is unclear.
Assumptions and Limitations of the Simulation Platform
The article relies on a physics-based simulation platform, which may not accurately capture the complexities of real-world AI systems and environments.
Expert Commentary
The article makes a valuable contribution to the field of AI reliability modeling by proposing a computationally efficient framework for assessing the reliability of AI systems. The use of a physics-based simulation platform is a creative solution to the challenge of data scarcity, and the novel composite likelihood expectation-maximization algorithm demonstrates promising results. However, the article's focus on autonomous vehicle perception systems limits the generalizability of the framework, and the assumptions and limitations of the simulation platform require further investigation. Overall, the article provides a useful starting point for researchers and practitioners seeking to improve the reliability and safety of AI-based systems.
Recommendations
- ✓ Future research should investigate the application of the proposed framework to other AI systems and domains to establish its generalizability.
- ✓ The development of more sophisticated simulation platforms and data generation methods is necessary to capture the complexities of real-world AI systems and environments.
