Fairness, Explainability & Causality
Subgroup performance with no universal fairness metric, explanations that are approximate, privacy as design, and the line between predicting Y and causing it.
One aggregate number hides that a model can be a different model for different groups. There is no single fairness metric to optimise; choosing one is a policy decision, and several of them cannot all hold at once.
An explanation describes the model, not the world, and describes a wrong model just as fluently. Know whether you need a global picture, a local reason or a counterfactual, and whether an interpretable model would make the question go away.
A model that predicts Y from X has learned that X and Y move together in data generated by an old policy. Acting on X to change Y is a different question, and usually needs an experiment rather than a model.
A training set is personal data, an artifact can memorise it, and a prediction log is a record of people. Privacy is a design property of the pipeline — minimisation, retention, access, and honest limits on anonymisation.
A human in the loop is a threshold, a queue, and a source of labels. Decide where the human decides, size the queue from the threshold, watch for automation bias, and remember that overrides are training data — and biased training data.