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Making ML models fairer through explanations: the case of LimeOut

Making ML models fairer through explanations: the case of LimeOut

International Joint Conference on the Analysis of Images, Social Networks and Texts (AISNT), 2020
1 November 2020
Guilherme Alves
Vaishnavi Bhargava
Miguel Couceiro
A. Napoli
    FaML
ArXiv (abs)PDFHTML

Papers citing "Making ML models fairer through explanations: the case of LimeOut"

5 / 5 papers shown
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration
Explanations as Bias Detectors: A Critical Study of Local Post-hoc XAI Methods for Fairness Exploration
Vasiliki Papanikou
Danae Pla Karidi
E. Pitoura
Emmanouil Panagiotou
Eirini Ntoutsi
525
3
0
01 May 2025
A Critical Survey on Fairness Benefits of Explainable AI
A Critical Survey on Fairness Benefits of Explainable AI
Luca Deck
Jakob Schoeffer
Maria De-Arteaga
Niklas Kühl
653
47
0
15 Oct 2023
Explanations, Fairness, and Appropriate Reliance in Human-AI
  Decision-Making
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-MakingInternational Conference on Human Factors in Computing Systems (CHI), 2022
Jakob Schoeffer
Maria De-Arteaga
Niklas Kuehl
FaML
551
84
0
23 Sep 2022
Fairness of Machine Learning Algorithms in Demography
Fairness of Machine Learning Algorithms in Demography
I. Emmanuel
E. Mitrofanova
FaML
206
0
0
02 Feb 2022
On Interpretability and Similarity in Concept-Based Machine Learning
On Interpretability and Similarity in Concept-Based Machine LearningInternational Joint Conference on the Analysis of Images, Social Networks and Texts (AISNT), 2021
L. Kwuida
D. Ignatov
132
3
0
25 Feb 2021
1
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