Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data

24 August 2019

Papers citing "Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data"

8 / 8 papers shown

Title
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 26 4 0 29 Apr 2024
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts Shubham Sharma Jette Henderson Joydeep Ghosh FedML MoE 11 5 0 10 Oct 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 30 11 0 13 May 2022
Fairness-Aware Online Meta-learning Chengli Zhao Feng Chen B. Thuraisingham FaML 21 34 0 21 Aug 2021
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 208 663 0 17 Feb 2018
Discriminatory Transfer Chao Lan Jun Huan FaML 192 18 0 03 Jul 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 243 11,568 0 09 Mar 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 185 2,079 0 24 Oct 2016