Classification
AI Development Lifecycle
Overview
Training, validation, and testing are standard data splits used in the lifecycle of machine learning and AI model development. The training set is used to fit the model's parameters; the validation set is used to tune hyperparameters and assess model performance during development; the testing set is used to evaluate the final model's performance on unseen data. A common split is 70% for training, 15% for validation, and 15% for testing, though the proportions can vary based on dataset size and domain. This methodology helps prevent overfitting and provides a more objective measure of how the model will perform in real-world scenarios. However, a limitation is that if the splits are not representative or if data leakage occurs between sets, model evaluation can be misleading. Additionally, in domains with limited data, achieving meaningful splits without sacrificing model performance can be challenging.
Governance Context
AI governance frameworks like the EU AI Act and NIST AI RMF emphasize the importance of robust evaluation procedures, including clear separation of training, validation, and test datasets. For example, the NIST AI Risk Management Framework requires documentation of data provenance and evaluation protocols to prevent data leakage and ensure reproducibility. The EU AI Act obliges providers of high-risk AI systems to maintain records of training and testing procedures, ensuring datasets are representative and free from bias. Concrete obligations include: (1) maintaining detailed documentation of how data is split and used at each stage, and (2) conducting regular audits to verify that data splits are properly implemented and that test sets remain independent. Organizations may also be required to ensure that datasets are regularly reviewed for representativeness and to implement controls that prevent data leakage between splits.
Ethical & Societal Implications
Improper data splitting can lead to overestimated model performance, resulting in deployment of unsafe or biased systems. This can exacerbate societal harms, such as discrimination in hiring or healthcare disparities. Transparent and well-documented data handling is essential for accountability and public trust. Ethical AI practice requires vigilance against shortcuts or oversights in data management, as these can have far-reaching consequences for individuals and communities affected by AI-driven decisions. Additionally, lack of proper splits undermines efforts to ensure fairness, safety, and explainability in AI outcomes.
Key Takeaways
Training, validation, and testing splits are foundational for trustworthy AI development.; Improper data splitting can result in overfitting, data leakage, and misleading performance metrics.; Governance frameworks require documentation and auditability of data management practices.; Representative and independent test sets are crucial for real-world reliability.; Ethical risks arise when poor data handling leads to biased or unsafe AI outcomes.; Regular audits and controls help ensure the integrity of data splits.; Transparent documentation of data splits supports compliance and public trust.