The Human Signal: Mining Override Explanations

Chapter 325 ## The Human Signal: Mining Override Explanations > *"Not every decision is right. But every decision has a story."* In Chapter 324, we ended with a directive: start logging overrides. Treat the "reasons" as a dataset. Now, let's ask why. ### The Bottleneck of Truth The discussion prompt posed a critical question: *What is the hardest part of capturing the 'reason' for an override?* Most teams answer this with fear. They worry about being wrong. They fear complexity. It is both, but honesty is the gatekeeper to complexity. A model can handle complexity. It cannot handle a lie. If a manager writes "Customer insisted" instead of "Policy violation," your feature set is broken. If they write "Competitor price lower," your model knows a pricing gap exists. As a data leader, you must protect the psychological safety required for honest data. ### Engineering the Human Voice Raw text is hard to feed into standard predictive models. But we are not there yet. 1. **Categorization:** Use Natural Language Processing (NLP) to tag reasons into buckets: "Risk," "Price," "Experience," "Compliance." 2. **Sentiment:** Are overrides driven by anger? Or by empathy? This affects model tuning. 3. **Frequency:** If 40% of overrides cite "Fraud," your model needs a new risk layer. ### The Strategy Loop Do not discard the override. It is the correction signal. Use the reason data to retrain. If the reason is "The customer was a VIP," and the model didn't see VIP status, your data pipeline is incomplete. Fix the pipeline. If the reason is "We can't process this," look for the regulatory constraint. Every override is a lesson. Collect it. ### Closing Start small. Implement the logging next week. Review it in your weekly meeting. Look at the "reasons" as a dataset. The model is not the master. The data is. *- Mo Yu Xing* > *End of Chapter 325.*