Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking-Reference-Cited by-同舟云学术

Help or Hinder? Evaluating the Impact of Fairness Metrics and Algorithms in Visualizations for Consensus Ranking

Published:2023-06-12 Issue: Volume: Page:
ISSN:
Container-title:2023 ACM Conference on Fairness, Accountability, and Transparency
language:
Short-container-title:

Author:

Shrestha Hilson¹^ORCID,Cachel Kathleen²^ORCID,Alkhathlan Mallak²^ORCID,Rundensteiner Elke²^ORCID,Harrison Lane¹^ORCID

Affiliation:

1. Computer Science, Worcester Polytechnic Institute, USA

2. Data Science, Worcester Polytechnic Institute, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3593013.3594108

Reference57 articles.

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2. Interactive Visualization for Group Decision Analysis

3. Themis-ml: A Fairness-Aware Machine Learning Interface for End-To-End Discrimination Discovery and Mitigation

4. Michael Behrisch James Davey Svenja Simon Tobias Schreck Daniel Keim and Jörn Kohlhammer. 2013. Visual comparison of orderings and rankings. In EuroVis. Michael Behrisch James Davey Svenja Simon Tobias Schreck Daniel Keim and Jörn Kohlhammer. 2013. Visual comparison of orderings and rankings. In EuroVis.

5. Rachel KE Bellamy , Kuntal Dey , Michael Hind , Samuel C Hoffman , Stephanie Houde , Kalapriya Kannan , Pranay Lohia , Jacquelyn Martino , Sameep Mehta , Aleksandra Mojsilovic , 2018. AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 ( 2018 ). Rachel KE Bellamy, Kuntal Dey, Michael Hind, Samuel C Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, 2018. AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018).