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

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

24 August 2019

Sorelle A. Friedler

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

11 / 11 papers shown

Title
CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models Song Wang Peng Wang Tong Zhou Yushun Dong Zhen Tan Jundong Li CoGe 44 6 0 02 Jul 2024
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle Luca Deck Astrid Schomacker Timo Speith Jakob Schöffer Lena Kästner Niklas Kühl 41 4 0 29 Apr 2024
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts Shubham Sharma Jette Henderson Joydeep Ghosh FedML MoE 21 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
Log-Euclidean Signatures for Intrinsic Distances Between Unaligned Datasets Tal Shnitzer Mikhail Yurochkin Kristjan Greenewald Justin Solomon 31 6 0 03 Feb 2022
A survey on datasets for fairness-aware machine learning Tai Le Quy Arjun Roy Vasileios Iosifidis Wenbin Zhang Eirini Ntoutsi FaML 11 240 0 01 Oct 2021
Fairness-Aware Online Meta-learning Chengli Zhao Feng Chen B. Thuraisingham FaML 29 34 0 21 Aug 2021
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 233 673 0 17 Feb 2018
Discriminatory Transfer Chao Lan Jun Huan FaML 200 20 0 03 Jul 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 281 11,681 0 09 Mar 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 201 2,082 0 24 Oct 2016