ISO/IEC 23053:2022 AI System ML Framework Management System

Framework standards for machine learning in artificial intelligence systems

ISO/IEC 23053 was first published in 2022 with the aim of establishing a scalable machine learning technology framework, with a focus on its innovative applications in the field of multimedia coding. The standard covers the following core contents:
1. Framework definition and architecture
Modular design: Define the functional module hierarchy of machine learning systems, including data preprocessing, model training, inference optimization, and result post-processing.
Interface specification: Standardize input/output interfaces (such as APIs, data formats) to ensure cross platform compatibility.
Performance evaluation indicators: Propose a quantitative evaluation system for model efficiency (such as FLOPS), inference delay, compression ratio, etc.

2. Multimedia encoding optimization
AI based encoding enhancement: using neural networks to optimize video/image compression algorithms (such as replacing traditional DCT transform).
Adaptive rate control: dynamically adjusting encoding parameters through ML models to improve visual quality in low bandwidth scenarios.
Metadata analysis: Standardize the storage and transmission format of content description information (such as object detection results) generated by machine learning.

3. Reliability and Security
Model interpretability: Require transparency reports on model decisions (such as feature importance analysis).
Data privacy protection: integrating differential privacy (DP), federated learning and other technical specifications.
Anti adversarial attack: Define robustness testing methods for adversarial samples.

1. Medical diagnosis
Application: ML based image analysis systems (such as X-ray recognition) need to follow the principle of interpretability, generate transparent diagnostic reports, and assist doctors in decision-making;
Compliance: Complies with medical device safety standards (such as ISO 13485) and ensures model robustness through adversarial sample testing.

2. Financial risk control
Data management: Implementing cross institutional data collaboration through federated learning technology, protecting user privacy while improving the accuracy of credit evaluation models;
Risk control: Regularly review model deviations to prevent discriminatory decisions caused by imbalanced training sets.

3. Intelligent manufacturing
Predictive maintenance: Utilizing ML frameworks to analyze sensor data in real-time, optimize equipment maintenance cycles, and reduce downtime;
Environment adaptation: the system needs to be compatible with diversified hardware (such as industrial robots, IoT devices) and edge computing environment.

4. Autonomous driving
Security verification: Multi modal data fusion testing (such as camera and radar data) ensures the reliability of decision-making under complex road conditions;
Continuous learning: Update models through OTA (Over the Air) technology to adapt to new traffic rules and driving scenarios.

（I.） Certification materials
Since ISO/IEC 23053:2022 does not involve certification processes, there are no certification materials available. However, organizations may need to prepare the following materials when implementing this standard:
AI System Description: Provide a detailed description of the organization's existing AI system architecture, functionality, and usage scenarios.
Machine learning model information: including model type (such as neural network, decision tree, etc.), input-output parameters, training dataset, performance indicators, etc.
Metadata record: Record the metadata of the model, such as the source of training data, the developers of the model, usage scenarios, etc.
Process documentation: covering the complete machine learning process from data acquisition, data preparation, modeling, validation and verification, model deployment to operation.

Implementation requirements
Model representation and exchange: According to standard requirements, machine learning models are structured to ensure seamless migration and sharing between different platforms and tools.
Metadata management: Establish a comprehensive metadata management system to record in detail the training data, performance indicators, usage scenarios, and other metadata of the model.
Optimize machine learning process: Improve the complete machine learning process from data acquisition, data preparation, modeling, validation and verification, model deployment to operation.
Continuous improvement: Pay attention to industry trends and standard updates, and timely integrate new requirements and best practices into the organization's AI system.

（II.） Application requirements
ISO/IEC 23053:2022 is not a certification standard, therefore there are no traditional "application conditions". However, organizations should meet the following conditions when implementing this standard:
Clear implementation intention: Organizations need to clarify the purpose of implementing this standard, such as improving the interoperability of AI systems, enhancing model transparency, etc.
Technical Capability: Organizations should possess corresponding technical capabilities, including the ability to develop, deploy, and manage machine learning models.
Resource support: Organizations need to invest necessary human, material, and financial resources to ensure the smooth implementation of standards