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Dimensions of Diversity in Human Perceptions of Algorithmic Fairness

Dimensions of Diversity in Human Perceptions of Algorithmic Fairness

2 May 2020
Nina Grgic-Hlaca
Gabriel Lima
Adrian Weller
Elissa M. Redmiles
    FaML
ArXivPDFHTML

Papers citing "Dimensions of Diversity in Human Perceptions of Algorithmic Fairness"

7 / 7 papers shown
Title
Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms
Laypeople's Attitudes Towards Fair, Affirmative, and Discriminatory Decision-Making Algorithms
Gabriel Lima
Nina Grgic-Hlaca
Markus Langer
Yixin Zou
FaML
44
0
0
12 May 2025
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Clara Punzi
Roberto Pellungrini
Mattia Setzu
F. Giannotti
D. Pedreschi
25
5
0
09 Feb 2024
Fair Enough? A map of the current limitations of the requirements to
  have "fair" algorithms
Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms
Alessandro Castelnovo
Nicole Inverardi
Gabriele Nanino
Ilaria Giuseppina Penco
D. Regoli
FaML
21
1
0
21 Nov 2023
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning
  for Democratic and Inclusive Advancements
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
Maryam Molamohammadi
Afaf Taik
Nicolas Le Roux
G. Farnadi
37
1
0
11 Jun 2023
"There Is Not Enough Information": On the Effects of Explanations on
  Perceptions of Informational Fairness and Trustworthiness in Automated
  Decision-Making
"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
Pitfalls of Explainable ML: An Industry Perspective
Pitfalls of Explainable ML: An Industry Perspective
Sahil Verma
Aditya Lahiri
John P. Dickerson
Su-In Lee
XAI
16
9
0
14 Jun 2021
Fair prediction with disparate impact: A study of bias in recidivism
  prediction instruments
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova
FaML
207
2,091
0
24 Oct 2016
1