Fairer and more accurate, but for whom?

30 June 2017

Papers citing "Fairer and more accurate, but for whom?"

25 / 25 papers shown

Title
Robustness and Cybersecurity in the EU Artificial Intelligence Act Henrik Nolte Miriam Rateike Michèle Finck 103 2 0 22 Feb 2025
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution Zhengmian Hu Tong Zheng Heng Huang BDL 88 2 0 29 Oct 2024
Interpretable Differencing of Machine Learning Models Swagatam Haldar Diptikalyan Saha Dennis L. Wei Rahul Nair Elizabeth M. Daly 61 1 0 10 Jun 2023
Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy Emily L. Aiken Esther Rolf J. Blumenstock 104 6 0 02 May 2023
Locating disparities in machine learning Moritz von Zahn O. Hinz Stefan Feuerriegel 56 5 0 13 Aug 2022
Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables Kenneth Holstein Maria De-Arteaga Lakshmi Tumati Yanghuidi Cheng 190 27 0 28 Jul 2022
Algorithmic Fairness in Business Analytics: Directions for Research and Practice Maria De-Arteaga Stefan Feuerriegel M. Saar-Tsechansky FaML 122 45 0 22 Jul 2022
Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions Chunxi Liu M. Picheny Leda Sari Pooja Chitkara Alex Xiao Xiaohui Zhang Mark Chou Andres Alvarado C. Hazirbas Yatharth Saraf 88 44 0 18 Nov 2021
SoK: Machine Learning Governance Varun Chandrasekaran Hengrui Jia Anvith Thudi Adelin Travers Mohammad Yaghini Nicolas Papernot 137 16 0 20 Sep 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 192 212 0 12 Jul 2021
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence Atoosa Kasirzadeh 70 24 0 01 Mar 2021
Characterizing Fairness Over the Set of Good Models Under Selective Labels Amanda Coston Ashesh Rambachan Alexandra Chouldechova FaML 105 85 0 02 Jan 2021
Fairness in Machine Learning: A Survey Simon Caton C. Haas FaML 108 653 0 04 Oct 2020
To Split or Not to Split: The Impact of Disparate Treatment in Classification Hao Wang Hsiang Hsu Mario Díaz Flavio du Pin Calmon 115 23 0 12 Feb 2020
Case Study: Predictive Fairness to Reduce Misdemeanor Recidivism Through Social Service Interventions Kit T. Rodolfa E. Salomon Lauren Haynes Iván Higuera Mendieta Jamie L Larson Rayid Ghani 47 47 0 24 Jan 2020
Automated Dependence Plots David I. Inouye Liu Leqi Joon Sik Kim Bryon Aragam Pradeep Ravikumar 64 1 0 02 Dec 2019
Fairness Sample Complexity and the Case for Human Intervention Ananth Balashankar Alyssa Lees 18 3 0 24 Oct 2019
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes Alexey Romanov Maria De-Arteaga Hanna M. Wallach J. Chayes C. Borgs Alexandra Chouldechova S. Geyik K. Kenthapadi Anna Rumshisky Adam Tauman Kalai 75 81 0 10 Apr 2019
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions Hao Wang Berk Ustun Flavio du Pin Calmon FaML 114 85 0 29 Jan 2019
Individual Fairness in Hindsight Swati Gupta Vijay Kamble FaML 86 63 0 10 Dec 2018
Investigating Human + Machine Complementarity for Recidivism Predictions S. Tan Julius Adebayo K. Quinn Ece Kamar FaML 69 54 0 28 Aug 2018
Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction C. Wadsworth Francesca Vera Chris Piech FaML 99 180 0 30 Jun 2018
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification Michael P. Kim Amirata Ghorbani James Zou MLAU 248 346 0 31 May 2018
Calibration for the (Computationally-Identifiable) Masses Úrsula Hébert-Johnson Michael P. Kim Omer Reingold G. Rothblum FaML 89 88 0 22 Nov 2017
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation S. Tan R. Caruana Giles Hooker Yin Lou MLAU 146 188 0 17 Oct 2017