Beware of it! Don't pollute my "little assistant"

At present, artificial intelligence has been deeply integrated into all aspects of economic and social development. While profoundly changing human production and lifestyle, it has also become a key area related to high-quality development and high-level security. However, there are problems with artificial intelligence training data, including false information, fictitious content, and biased opinions, which cause data source pollution and bring new challenges to artificial intelligence security.

Data is the foundation of artificial intelligence

The three core elements of artificial intelligence are algorithms, computing power and data. Data is the basic element for training AI models and the core resource for AI applications.

——Provide raw materials for AI models. Massive data provides sufficient training materials for AI models, allowing them to learn the inherent laws and patterns of data and achieve semantic understanding, intelligent decision-making and content generation. At the same time, data also drives artificial intelligence to continuously optimize performance and accuracy, and realize iterative upgrades of models to adapt to new needs.

——Affect the performance of AI model. AI models have extremely high requirements on the quantity, quality and diversity of data. Sufficient data volume is the prerequisite for fully training large-scale models; data with high accuracy, completeness and consistency can effectively avoid misleading the model; diversified data covering multiple fields can improve the model's ability to cope with actual complex scenarios.

——Promote the application of AI models. The increasing abundance of data resources has accelerated the implementation of the "artificial intelligence " action and effectively promoted the in-depth integration of artificial intelligence with various economic and social fields. This not only cultivates and develops new productive forces, but also promotes the leap-forward development of my country's science and technology, industrial optimization and upgrading, and the overall jump in productivity.

Data pollution impacts security defense lines

High-quality data can significantly improve the accuracy and reliability of the model, but once the data is contaminated, it may lead to model decision-making errors or even AI system failure, posing certain security risks.

——Publish harmful content. Contaminated data generated through "data poisoning" behaviors such as tampering, fabrication, and repetition will interfere with the parameter adjustment of the model during the training phase, weaken the model's performance, reduce its accuracy, and even induce harmful output. Research shows that when there are only 0.01% false text in the training data set, the harmful content output by the model will increase by 11.2%; even if there is 0.001% false text, the harmful output will increase by 7.2% accordingly.

——Causes recursive pollution. False content generated by artificial intelligence contaminated by data may become a data source for subsequent model training, forming a continuing "contamination legacy effect." Currently, the quantity of AI-generated content on the Internet far exceeds the real content produced by humans. A large amount of low-quality and non-objective data floods it, leading to the accumulation of erroneous information in AI training data sets from generation to generation, ultimately distorting the cognitive capabilities of the model itself.

——Trigger real risks. Data pollution may also trigger a series of real risks, especially in areas such as financial markets, public safety, and medical health. In the financial field, criminals use AI to concoct false information, causing data pollution, which may cause abnormal fluctuations in stock prices and pose new market manipulation risks; in the public safety field, data pollution can easily disturb public perception, mislead public opinion, and induce social panic; in the medical and health field, data pollution may cause the model to generate incorrect diagnosis and treatment recommendations, which not only endangers the safety of patients, but also aggravates the spread of pseudoscience.

Build a solid artificial intelligence data base

——Strengthen source supervision to prevent the generation of pollution. Based on laws and regulations such as the Cybersecurity Law, Data Security Law, and Personal Information Protection Law, an AI data classification and hierarchical protection system is established to fundamentally prevent the generation of contaminated data and help effectively prevent AI data security threats.

——Strengthen risk assessment and ensure data circulation. Strengthen the overall assessment of artificial intelligence data security risks to ensure data security throughout the entire life cycle, including collection, storage, transmission, use, exchange and backup. Simultaneously accelerate the construction of an artificial intelligence security risk classification management system and continuously improve the comprehensive data security capabilities.

——End cleaning and repair, building a governance framework. Regularly clean and repair contaminated data in accordance with regulatory standards. Develop specific rules for data cleaning in accordance with relevant laws, regulations and industry standards. Gradually build a modular, monitorable, and scalable data governance framework to achieve continuous management and quality control.

Under the strong leadership of the Party Central Committee with Comrade Xi Jinping as the core, the national security agencies will fully implement the overall national security concept, work with relevant departments to prevent data pollution risks in the field of artificial intelligence in China, maintain artificial intelligence security and data security in accordance with the law, and continuously build a strong national security barrier.