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Counterfactual explanations (CFEs) are an emerging technique under theumbrella of interpretability of machine learning (ML) models. They provide``what if'' feedback of the form ``if an input datapoint were $x'$ instead of$x$, then an ML model's output would be $y'$ instead of $y$.'' Counterfactualexplainability for ML models has yet to see widespread adoption in industry. Inthis short paper, we posit reasons for this slow uptake. Leveraging recent workoutlining desirable properties of CFEs and our experience running the ML wingof a model monitoring startup, we identify outstanding obstacles hindering CFEdeployment in industry.

Counterfactual Explanations for Machine Learning: Challenges Revisited