Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models

17 December 2020

Papers citing "Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models"

8 / 8 papers shown

Title
Generative AI Voting: Fair Collective Choice is Resilient to LLM Biases and Inconsistencies Srijoni Majumdar Edith Elkind Evangelos Pournaras SyDa 49 1 0 31 May 2024
My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning Aimen Gaba Zhanna Kaufman Jason Chueng Marie Shvakel Kyle Wm. Hall Yuriy Brun Cindy Xiong Bearfield 27 14 0 07 Aug 2023
Fair Enough: Searching for Sufficient Measures of Fairness Suvodeep Majumder Joymallya Chakraborty Gina R. Bai Kathryn T. Stolee Tim Menzies 16 26 0 25 Oct 2021
Blindspots in Python and Java APIs Result in Vulnerable Code Yuriy Brun Tian Lin J. Somerville Elisha M Myers Natalie C. Ebner 19 7 0 10 Mar 2021
Astraea: Grammar-based Fairness Testing E. Soremekun Sakshi Udeshi Sudipta Chattopadhyay 24 27 0 06 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 192 742 0 13 Dec 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016