Training Data

Training data is the foundational dataset used to train machine learning models, containing input data and corresponding correct outputs (labels). Its quality and representativeness directly determine the final model's accuracy. Within risk management, training data is a primary source of AI system risk. According to ISO/IEC 23894:2023 (AI Risk Management), organizations must manage data quality, bias, and traceability. If personal information is involved, compliance with regulations like GDPR, which requires a lawful basis for processing, is mandatory. It differs from "validation data" and "test data," which are used to objectively evaluate model performance without being used to adjust model parameters during the training phase.

Effective management of training data is crucial for mitigating AI adoption risks. A practical three-step approach includes: 1. Data Sourcing Due Diligence: Before acquisition, assess copyright status, licensing terms, and the presence of sensitive personal data. 2. Establish a Data Governance Framework: Based on standards like ISO 27001, define policies for data classification, access control, and lifecycle management. 3. Implement Bias Detection and Mitigation: Use algorithmic tools to analyze training data for potential biases and apply techniques like data augmentation for correction. For instance, a financial institution developing a credit scoring model used this process to ensure fairness, achieving a 99%+ model audit pass rate and reducing customer complaints from misjudgments by 30%.

Taiwan enterprises face three key challenges with training data. First, "Regulatory Uncertainty": The application of Taiwan's Copyright Act and Personal Data Protection Act to AI training's "fair use" remains ambiguous. Second, "Scarcity of High-Quality Local Data": A lack of licensed, well-annotated datasets reflecting local contexts hinders model performance. Third, "Technical and Resource Gaps": SMEs often lack the data science and legal expertise to implement robust data governance. To overcome this, companies should establish an AI ethics committee to set internal policies, invest in data cleansing and anonymization, and partner with expert consultants to deploy a lightweight AI risk management framework.

Winners Consulting specializes in training data for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact