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Whether or not we can ever come up with a good definition of "explainability", I still think it's misguided. In almost all applications of ML, accuracy matters
by Houshalter 10y ago
Whether or not we can ever come up with a good definition of "explainability", I still think it's misguided. In almost all applications of ML, accuracy matters far, far more. Predicting whether a patient has cancer, for instance, it matters a great deal that you get the highest accuracy possible. Every point in accuracy you lose to "explainability" means people die.
- romaniv 10y agoUntil your model starts misdiagnosing people in real life because your training data wasn't 100% perfect (which it never will be for complex real-life problems). https://vimeo.com/125940125 https://vimeo.com/125940125 The problem with unexplainable AI isn't just the lack of explanations. It's the fact that the entire model is a black box, so you cannot somehow tweak a single part of it without retraining.