New tool helps people choose the right method for evaluating AI models
When machine-learning models are deployed in real-world situations, perhaps to flag potential disease in X-rays for a radiologist to review, human users need to know when to trust the model’s predictions. But machine-learning models are so large and complex that even the scientists who design them don’t understand exactly how the models make predictions. So, they create techniques known as…