MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to test whenever a model is deployed in a new setting.
Mistaken correlations: Why it’s critical to move beyond overly aggregated machine-learning metrics
