Knowledgebase

Responsible and Applied Practice: Everything That Matters, Briefly Print

  • machinelearningengineering, machine, performance, guide, howto, solution, zillionkinghost, hosting
  • 0

The summary.

REMOVING PROTECTED ATTRIBUTES DOES NOT MAKE A MODEL FAIR

Other features correlate with them. Measure performance disaggregated by group, and choose a fairness definition deliberately — several cannot hold at once.

Never claim a model is fair without stating what that means.

MODELS ARE REUSED FOR PURPOSES THEY WERE NEVER EVALUATED FOR

Which is why documenting intended use, and what the model must not be used for, matters more than documenting architecture.

Never deploy a model nobody can describe the limits of.

OVERSIGHT REQUIRES INFORMATION, TIME AND AUTHORITY TO DISAGREE

Without all three it is rubber-stamping. A very low disagreement rate means either an excellent model or oversight that is not happening.

High appeal overturn rates mean the model should not be deployed as it is.

BUILD THE THINNEST END-TO-END PATH FIRST, WITH A TRIVIAL MODEL

The path is the real risk, not the model. Months of modelling before anyone has seen a prediction in place is how projects die.

AGREE WHAT PERFORMANCE IS GOOD ENOUGH BEFORE BUILDING

Afterwards, the answer becomes whatever was achieved.

BE PREPARED TO RECOMMEND NOT DEPLOYING


Was this answer helpful?
Back

Are you happy with your experience? Leave us a review on Trustpilot.


Trustpilot