Introduction to Fairness in Algorithmic Decision Making mini-track

Abstract

A vast application of machine learning and decision-making algorithms for decision support in various areas of life caused the need for the algorithms to take into account additional constraints, such as non-discriminatory behavior or imposing fairness, or social welfare prior to proposing decisions to decision makers. These constraints can be fulfilled by carefully guiding the whole decision-making and data governance process, by adjusting decision-making, data mining and machine learning algorithms to fulfill additional constraints. For example, by adapting CRISP-DM methodology to account for possible biases, by imposing instance-dependent cost-sensitive learning, or enforcing equality in data envelopment analysis as presented in this mini-track.