Developer Builds ContextLens Tool to Surface Hidden Judgment in ML Workflows
A PhD researcher in context-aware intelligent systems recently built ContextLens, a Streamlit app designed to audit tabular datasets before any machine learning model is trained. The tool profiles uploaded data and flags structural issues such as missing values, class imbalance, duplicate rows, and potential identifier columns that could leak answers into a model. Unlike typical ML tutorials that skip straight to training, ContextLens first asks whether the modelling approach makes sense given the data's structure. Its decision logic is built on explicit, inspectable rules rather than opaque AI methods, meaning users can read exactly why a recommendation was made. The developer says the project grew directly from their PhD research into how systems can use environmental structure — rather than hidden intelligence — to make sound, transparent decisions.
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