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Fairness and Transparency in Recommendation: The Users' Perspective

Fairness and Transparency in Recommendation: The Users' Perspective

16 March 2021
Nasim Sonboli
Jessie J. Smith
Florencia Cabral Berenfus
Robin Burke
Casey Fiesler
    FaML
ArXivPDFHTML

Papers citing "Fairness and Transparency in Recommendation: The Users' Perspective"

6 / 6 papers shown
Title
Diffusion Models in Recommendation Systems: A Survey
Diffusion Models in Recommendation Systems: A Survey
Ting-Ruen Wei
Yi Fang
87
2
0
20 Feb 2025
Towards Individual and Multistakeholder Fairness in Tourism Recommender
  Systems
Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
Ashmi Banerjee
Paromita Banik
Wolfgang Wörndl
21
11
0
05 Sep 2023
Elucidate Gender Fairness in Singing Voice Transcription
Elucidate Gender Fairness in Singing Voice Transcription
Xiangming Gu
Weizhen Zeng
Ye Wang
17
3
0
05 Aug 2023
Work with AI and Work for AI: Autonomous Vehicle Safety Drivers' Lived
  Experiences
Work with AI and Work for AI: Autonomous Vehicle Safety Drivers' Lived Experiences
Mengdi Chu
Keyu Zong
Xin Shu
Jiangtao Gong
Zhicong Lu
Kaimin Guo
Xinyi Dai
Guyue Zhou
36
17
0
09 Mar 2023
Recommender Systems and Algorithmic Hate
Recommender Systems and Algorithmic Hate
Jessie J. Smith
Lucia Jayne
Robin Burke
16
11
0
05 Sep 2022
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
251
3,683
0
28 Feb 2017
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