How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees

22 June 2020

Papers citing "How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees"

4 / 4 papers shown

Title
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Luke E. Richards Jessie Yaros Jasen Babcock Coung Ly Robin Cosbey Timothy Doster Cynthia Matuszek NAI 66 0 0 13 Feb 2025
Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments Angie Boggust Venkatesh Sivaraman Yannick Assogba Donghao Ren Dominik Moritz Fred Hohman VLM 58 3 0 06 Aug 2024
A survey on datasets for fairness-aware machine learning Tai Le Quy Arjun Roy Vasileios Iosifidis Wenbin Zhang Eirini Ntoutsi FaML 11 239 0 01 Oct 2021
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,090 0 24 Oct 2016