Algorithm Failure Atlas (AFA): A Unified Framework for Mapping, Analyzing, and Mitigating Algorithmic Vulnerabilities in AI Systems

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

As artificial intelligence and automated decision-making systems become deeply integrated into critical infrastructure, high-stakes finance, autonomous driving, and healthcare, ensuring their reliability and robustness is paramount. Traditional evaluation methodologies typically rely on aggregate performance metrics such as overall accuracy, precision, recall, or mean squared error over a test distribution. However, these global metrics frequently obscure localized vulnerabilities, systematic failure modes, and critical blind spots within the input space. To bridge this fundamental gap, this paper introduces the Algorithm Failure Atlas (AFA), a rigorous mathematical and conceptual framework designed to systematically map, characterize, and predict algorithmic failures across complex input spaces. The core philosophy of the Algorithm Failure Atlas is to treat algorithm reliability not as a static, global property, but as a localized, conditional probability distribution defined over the input domain. By formalizing the failure mapping function as M_A(x) = Pr(A fails | x), the AFA framework enables automated systems to discern precisely where an algorithm is structurally reliable, where it exhibits high instability, and under what exact conditions execution should be safely transferred to alternative algorithms or human operators. This paper provides a comprehensive formulation of the AFA framework, detailing its mathematical foundations, spatial modeling techniques, computational construction algorithms, decision-theoretic switching mechanisms, and extensive theoretical properties. Through this paradigm shift, the Algorithm Failure Atlas lays a foundational stone for the next generation of trustworthy, safe, and robust artificial intelligence systems.

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