Why Data Governance Is the Key to Trustworthy AI in the Large Model Era

1. AI Data Governance: Concept Definition

Since 2021, generative AI represented by large models has transformed production and life, shifting AI development from a model‑centric to a data‑centric approach. High‑quality, large‑scale, diverse data are essential, but practitioners face data security, privacy leaks, bias, and the problem of "high volume, low quality" data, which can hinder AI progress and threaten safety.

2. Evolution of Data Governance

Data governance originated in enterprise management. Definitions vary across institutions, but common elements include decision‑making authority, responsibility allocation, data quality, security, and compliance. International standards (ISO/TR 14872, GB/T 35295‑2017) emphasize coordination, responsibility, resource scheduling, and governance of data quality, security, and compliance.

3. Three Stages of Data Governance

1980s: Early data quality management (TDQM) with DBMS.

2000s: Data warehouses, master data management, BI platforms.

2020s: Large‑model era introduces new challenges and requirements.

4. Challenges of Data Governance in the Large‑Model Era

1) High‑volume, low‑quality data : Large models rely on massive, often uncontrolled internet data, leading to quality gaps and lack of evaluation methods for multimodal, unstructured data.

2) Security and privacy leaks : Full‑life‑cycle AI development involves data over‑collection, bias, poisoning, and other risks that can harm individuals, enterprises, and societies.

3) Bias and discrimination : Generative models inherit biases from training data, producing unfair or harmful content.

To address these, the concept of Data Governance for Artificial Intelligence (DG4AI) has emerged, aiming to manage data throughout the AI lifecycle.

5. Definition of AI‑Oriented Data Governance

AI is a multidisciplinary field enabling computers to perform high‑level functions. Data governance is the organizational management of data to ensure quality and security. DG4AI therefore means managing and controlling data in AI applications to guarantee quality, reliability, security, and compliance while protecting privacy.

6. Main Phases and Objects of AI‑Oriented Data Governance

Top‑Level Design : Establish overall framework and strategic goals aligned with organizational objectives.

Organizational Assurance System : Provide resources (people, compute, algorithms, data, technology, management) and build policies and standards.

Engineering Construction : Implement data collection, preprocessing/cleaning, feature engineering, annotation, splitting, augmentation, model training, validation, testing, and inference.

Operation Optimization & AI Integration : Scale AI deployment, create a virtuous loop between data governance and AI applications.

Data objects range from raw multimodal datasets to training, validation, test, and inference datasets.

7. Value of AI‑Oriented Data Governance

Improves AI model accuracy and reliability.

Shortens development cycles and reduces costs.

Enhances overall system security and contributes to a comprehensive data governance theory.

8. Principles of AI‑Oriented Data Governance

Standardization : Flexible, operable standards and processes to reduce cost and improve efficiency.

Transparency : Explainable and understandable data handling to build trust.

Compliance : Align with laws and industry standards.

Security : Encryption, access control, and other safeguards.

Responsibility : Ethical standards, privacy respect, and non‑discrimination.

Fairness : Equal treatment of all users.

Auditability : Monitoring and recording of data lifecycle activities.

9. Key Work Areas for AI Data Governance

9.1 Data Quality Governance

Identify requirements, set quality targets, establish quality management systems, evaluate data sources, perform preprocessing/cleaning, annotation, augmentation, feature engineering, bias detection & correction, and continuous monitoring across training and inference stages.

9.2 Data Security & Privacy Governance

Implement full‑life‑cycle security supervision, risk‑based classification, encryption, differential privacy, homomorphic encryption, anonymization, concept erasure, and regular compliance audits.

9.3 Data Ethics Governance

Formulate ethical policies, ensure transparency and explainability, regulate data collection and annotation, conduct bias detection & mitigation, and perform regular risk assessments and updates.

10. Outlook

AI data industry will achieve clearer division of labor, reducing redundant labeling and collection.

Effective data governance will become a decisive advantage for large‑model competition.

DG4AI will mature into standardized, service‑oriented solutions delivering high‑quality, secure data products.