Uploaded October 2020 | Updated September 2026, 1 day ago
Machine learning often seems like a black box: you train a model, adjust some hyperparameters, and get a prediction back. It’s often hard to know exactly what led your model to a specific prediction. In this talk I’ll highlight some tools available for digging into the signals behind your model’s predictions. Then I’ll look at how we can use this data to improve our models to ensure they treat all users fairly.
Machine learning often seems like a black box: you train a model, adjust some hyperparameters, and get a prediction back. It’s often hard to know exactly what led your model to a specific prediction. In this talk I’ll highlight some tools available for digging into the signals behind your model’s predictions. Then I’ll look at how we can use this data to improve our models to ensure they treat all users fairly.
