Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda
for Developing Practical Guidelines and Tools

Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools

29 September 2023

Maximilian Schambach

Rakshit Naidu

Kit T. Rodolfa

Hoda Heidari

Papers citing "Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools"

16 / 16 papers shown

Title
Addressing Discretization-Induced Bias in Demographic Prediction Evan Dong Aaron Schein Yixin Wang Nikhil Garg 27 3 0 27 May 2024
Fast online ranking with fairness of exposure Nicolas Usunier Virginie Do Elvis Dohmatob 24 18 0 13 Sep 2022
It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games Benjamin Fish Luke Stark FaML 14 8 0 12 Sep 2022
Fair Representation Learning through Implicit Path Alignment Changjian Shui Qi Chen Jiaqi Li Boyu Wang Christian Gagné 36 27 0 26 May 2022
Adaptive Sampling Strategies to Construct Equitable Training Datasets William Cai R. Encarnación Bobbie Chern S. Corbett-Davies Miranda Bogen Stevie Bergman Sharad Goel 77 29 0 31 Jan 2022
Controlling Directions Orthogonal to a Classifier Yilun Xu Hao He T. Shen Tommi Jaakkola 56 19 0 27 Jan 2022
Can Information Flows Suggest Targets for Interventions in Neural Circuits? Praveen Venkatesh Sanghamitra Dutta Neil Mehta P. Grover AAML 24 8 0 09 Nov 2021
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning Zhaowei Zhu Tianyi Luo Yang Liu 148 39 0 12 Oct 2021
Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework Matan Halevy Camille Harris A. Bruckman Diyi Yang A. Howard 34 35 0 27 Sep 2021
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data Georgi Ganev Bristena Oprisanu Emiliano De Cristofaro 29 57 0 23 Sep 2021
Adaptive Sampling for Minimax Fair Classification S. Shekhar Greg Fields Mohammad Ghavamzadeh T. Javidi FaML 27 36 0 01 Mar 2021
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information Pranjal Awasthi Alex Beutel Matthaeus Kleindessner Jamie Morgenstern Xuezhi Wang FaML 49 55 0 16 Feb 2021
Towards Fair Deep Anomaly Detection Hongjing Zhang Ian Davidson FaML 47 38 0 29 Dec 2020
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference Disi Ji Padhraic Smyth M. Steyvers 34 43 0 19 Oct 2020
Improving fairness in machine learning systems: What do industry practitioners need? Kenneth Holstein Jennifer Wortman Vaughan Hal Daumé Miroslav Dudík Hanna M. Wallach FaML HAI 192 730 0 13 Dec 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 210 663 0 17 Feb 2018