90 citations · 124 across the 10 of their papers we have counts for
3 papers · 1 filter
Fair-OBNC: Correcting Label Noise for Fairer Datasets
Inês Oliveira e Silva, Sérgio Jesus, Hugo Ferreira +4
Data used by automated decision-making systems, such as Machine Learning models, often reflects discriminatory behavior that occurred in the past. These biases in the training data…
Aequitas Flow: Streamlining Fair ML Experimentation
Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva +5
Aequitas Flow is an open-source framework and toolkit for end-to-end Fair Machine Learning (ML) experimentation, and benchmarking in Python. This package fills integration gaps tha…
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Jean V. Alves, Diogo Leitão, Sérgio Jesus +5
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier. E…