Enhancing Fairness in Neural Networks Using FairVIC

arXiv:2404.18134v1 Announce Type: new
Abstract: Mitigating bias in automated decision-making systems, specifically deep learning models, is a critical challenge in achieving fairness. This complexity stems from factors such as nuanced definitions of fairness, unique biases in each dataset, and the trade-off between fairness and model accuracy. To address such issues, we introduce FairVIC, an innovative approach designed to enhance fairness in neural networks by addressing inherent biases at the training stage. FairVIC differs from traditional approaches that typically address biases …

abstract accuracy arxiv automated bias biases challenge complexity cs.ai cs.cy cs.lg dataset decision decision-making systems deep learning definitions fairness making model accuracy networks neural networks stat.ml systems trade trade-off type unique