Predicting Fairness of ML Software Configuration

arXiv:2404.19100v1 Announce Type: cross
Abstract: This paper investigates the relationships between hyperparameters of machine learning and fairness. Data-driven solutions are increasingly used in critical socio-technical applications where ensuring fairness is important. Rather than explicitly encoding decision logic via control and data structures, the ML developers provide input data, perform some pre-processing, choose ML algorithms, and tune hyperparameters (HPs) to infer a program that encodes the decision logic. Prior works report that the selection of HPs can significantly influence fairness. However, …

abstract algorithms applications arxiv control cs.ai cs.cy cs.lg cs.se data data-driven decision developers encoding fairness logic machine machine learning ml algorithms paper pre-processing processing relationships software solutions technical type via