What Building ContextLens Taught Me About Context-Aware Systems

Short summary

The author built ContextLens, a Streamlit app that profiles tabular datasets and provides inspectable, rule-based guidance on modeling decisions before training. The project connects to their PhD research on context-aware systems, demonstrating that domain-agnostic structural checks can surface meaningful risks like class imbalance, identifier leakage, and missingness without needing domain-specific knowledge. The author honestly acknowledges the tool's limits, noting that structural profiling can raise questions but cannot replace domain expertise or robust validation.

• •ContextLens profiles datasets and flags structural risks using explicit, inspectable rules rather than black-box AI
• •Domain-agnostic checks worked across both generic and healthcare datasets without domain-specific rules
• •The tool surfaces modeling questions but cannot replace domain expertise or rigorous validation

Generated with AI, which can make mistakes.