Fairness and bias in artificial intelligence - Big Data and AI Toronto

We can also make the algorithm set a different threshold for different sub-groups, making it easier or harder for a subgroup to get an optimistic prediction. Adopting such a technique will help offset the historical disadvantage one subgroup has over the other. Dr. Thomas shared one last thought with the audience. "You need to ensure fairness in your AI models. Don't assume that the algorithm is just going to do it properly, that the data is probably clean enough, or that it's not going to matter that much. Everyone needs to solve this very important problem by taking active measures to assess whether your model's predictions are fair and to fix it using some of these techniques."