Cognitive Sovereignty Self-Audit for Data Scientists
This audit measures whether you retain statistical intuition and domain reasoning when building models, or whether AI tools have replaced your own critical thinking. Your score reveals how much you rely on benchmarks and automation versus your ability to catch when a model is technically correct but practically wrong.
Before accepting any AutoML result, ask yourself what assumption it is making about your data. If you cannot answer that, you do not understand the model.
Code generation tools are fastest when you already know exactly what you want to write. If you need the tool to tell you what to write, you are not ready to write it.
Keep a log of models that worked in benchmarks but failed in production. The pattern in these failures is where your domain knowledge lives.
When an LLM suggests a feature, force yourself to explain why that feature should help the model before you test it. This one habit stops fragility.
Statistical intuition comes from noticing when a model is technically right but practically suspicious. Train this by regularly reading your error distributions instead of just your accuracy scores.