Algorithmic Fairness - Fair Regression Algorithms
Algorithmic fairness involves expressing notions such as equity, equality, or reasonable treatment, as quantifiable measures that a machine learning algorithm can optimise. Mathematising these concepts, so they can be inferred from data is challenging, as is deciding on the balance between fairness and other objectives such as accuracy in a particular application. My research in this area along with others at the Gradient Institute has thus far focused on regression algorithms. Measuring the fairness of a regression algorithm is difficult compared to the classification case for many popular fairness criteria. Similarly, adjusting the predictions of a regressor is more complex than doing so for a classifier, and so our research has been targeting these areas. Here you can read more about measurement, and adjusting regression algorithms.
