Ground Truth Management

Hex has evolved its machine learning capabilities by introducing comprehensive strategies and best practices for managing ground truth. This includes defining ground truth, exploring its types (human annotation, expert annotation, behavioral inference, synthetic ground truth), and detailing its role across the ML lifecycle (training, validation, testing, production monitoring). The post highlights the challenges of ground truth, such as label leakage, annotation drift, feedback loops, and distribution mismatch. It also addresses the specific challenges of ground truth for AI analytics, emphasizing the importance of context and governed semantic models. Best practices include treating ground truth as infrastructure, investing in data quality monitoring, and establishing clear processes for data collection, labeling, and validation.