三维天地牵头研制神经网络可解释评估标准，破解AI"黑箱"难题

Core Viewpoint - The release of the "Key Characteristics Explainable Evaluation Requirements for Neural Networks" standard marks a significant advancement in the standardization of artificial intelligence in China, addressing the challenges of explainability and reliability in neural networks [1][2] Group 1: Standard Overview - The standard was developed by Beijing Sanwei Tiandi Technology Co., Ltd. in collaboration with several authoritative institutions and will be implemented starting January 1, 2026 [1] - It establishes the first systematic norm in the field of neural network explainability assessment, filling a gap in both domestic and international standards [1] Group 2: Challenges Addressed - The standard aims to tackle issues such as operational efficiency, noise resistance, and the prevalent "black box" problem in neural networks, which hinder the reliable application of AI in critical areas like industrial production, autonomous driving, and medical diagnosis [1] - Enhancing the explainability, reliability, and applicability of neural networks is identified as a core direction for promoting high-quality development in AI and implementing national strategic deployments [1] Group 3: Evaluation Framework - The standard focuses on key performance aspects such as structural redundancy, noise resistance, and predictive reliability, introducing a comprehensive explainable evaluation framework [1] - It specifies evaluation methods, input requirements, operational processes, quantitative indicators, performance grading, and implementation norms, providing a unified metric for objectively measuring core neural network performance [1] Group 4: Expert Insights - Industry experts note that while techniques like network pruning and robustness evaluation have been widely studied, existing methods often lack explainability and fail to ensure objective fairness, limiting their credibility and practicality [2] - The new standard aims to create a multi-level, fully objective, and explainable key performance evaluation system, enabling real-time monitoring and assessment throughout the entire lifecycle of neural network design, training, deployment, and maintenance [2] - Experts agree that the innovative evaluation methods and techniques proposed in the standard reach an internationally advanced level, enhancing the reliability and transparency of AI products and fostering trust among users [2]