人工智能训练数据清洗安全指南

Investment Rating - The report does not provide a specific investment rating for the industry. Core Insights - The document outlines the importance of training data cleansing as a critical step in ensuring the quality of AI model training, emphasizing the need to eliminate content that violates core socialist values, discriminatory content, and any illegal or infringing information [8][11]. - It presents safety principles for training data cleansing, including safety control, diversity of data sources, transparency, and continuous iteration [17]. - The report identifies various risks associated with training data, such as data quality risks, risks of violating socialist core values, discriminatory content risks, commercial legal risks, and risks of infringing on others' legal rights [18][20][21][23][24]. Summary by Sections 1. Scope - The document provides guidelines applicable to all entities involved in training data cleansing, including AI model developers and data suppliers [11]. 2. Terminology Definitions - Key terms are defined, including "training data," "training data cleansing activities," and "security of training data cleansing process," which are essential for understanding the context of the guidelines [12][13]. 3. Safety Principles for Training Data Cleansing - The principles include: - Safety and control: Ensuring data legality and content safety [17]. - Diversity: Considering various data sources and scenarios [17]. - Transparency: Documenting rules and decisions for auditability [17]. - Continuous iteration: Adapting strategies based on feedback and changes [17]. 4. Risk Identification Dimensions - The report categorizes risks into several dimensions, including: - Data quality risks: Issues like incompleteness, inaccuracies, and outdated data [18]. - Violations of socialist core values: Content that could negatively impact society [20]. - Discriminatory content risks: Systematic biases against specific groups [21]. - Commercial legal risks: Risks associated with intellectual property violations [23]. - Infringement of legal rights: Risks related to personal information and other rights [24]. 5. Cleansing Methods - Various methods for data cleansing are outlined, including: - Data quality processing: Addressing completeness, accuracy, timeliness, usability, and redundancy [31]. - Source control: Ensuring data comes from reliable and verified sources [33]. - Content safety review: Implementing filtering rules and models for harmful content [35]. - Personal information protection: Techniques for anonymization and de-identification [36]. - Bias mitigation: Strategies to balance representation in training data [38]. 6. Implementation Process - The report details a structured process for implementing data cleansing, including: - Data collection and source review: Defining the scope and purpose of data collection [41][42]. - Initial data quality processing: Checking for completeness, accuracy, and relevance [43][44]. - Risk identification and cleansing strategy formulation: Using statistical and technical methods for risk assessment [48][49]. - Data cleansing execution: Following established methods to cleanse data [51]. - Secondary risk assessment: Evaluating the effectiveness of cleansing methods [52][53][54][55][56]. - Continuous monitoring and iterative optimization: Establishing feedback mechanisms and regular reviews [62][64].