This Thing Called Fairness: Disciplinary Confusion Realizing a Value in
Technology

This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology

26 September 2019

Joshua A. Kroll

Richmond Y. Wong

Papers citing "This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology"

14 / 14 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 45 4 0 29 Apr 2024
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model Teresa Scantamburlo Joachim Baumann Christoph Heitz FaML 33 5 0 09 Oct 2023
Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service" Kornel Lewicki M. S. Lee Jennifer Cobbe Jatinder Singh 36 21 0 02 Feb 2023
System Safety Engineering for Social and Ethical ML Risks: A Case Study Edgar W. Jatho L. Mailloux Shalaleh Rismani Eugene D. Williams Joshua A. Kroll 31 2 0 08 Nov 2022
"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making Jakob Schoeffer Niklas Kuehl Yvette Machowski FaML 39 52 0 11 May 2022
A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making Jakob Schoeffer FaML 34 3 0 29 Apr 2022
Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems Avinash Agarwal Harshna Agarwal Nihaarika Agarwal 42 28 0 10 Jan 2022
Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP Anna Rogers Timothy Baldwin Kobi Leins 104 64 0 14 Sep 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 256 491 0 31 Dec 2020
Social Biases in NLP Models as Barriers for Persons with Disabilities Ben Hutchinson Vinodkumar Prabhakaran Emily L. Denton Kellie Webster Yu Zhong Stephen Denuyl 22 302 0 02 May 2020
Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects Samir Passi S. Jackson 171 108 0 09 Feb 2020
Data Vision: Learning to See Through Algorithmic Abstraction Samir Passi S. Jackson 140 111 0 09 Feb 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 195 742 0 13 Dec 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,091 0 24 Oct 2016