Sampling Individually-Fair Rankings that are Always Group Fair-Reference-Cited by-同舟云学术

Sampling Individually-Fair Rankings that are Always Group Fair

Published:2023-08-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society
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Author:

Gorantla Sruthi¹^ORCID,Mehrotra Anay²^ORCID,Deshpande Amit³^ORCID,Louis Anand⁴^ORCID

Affiliation:

1. Computer Science and Automation, Indian Institute of Science, India

2. Yale University, USA

3. Microsoft, India

4. Indian Institute of Science, India

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3600211.3604671

Reference69 articles.

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2. Incremental cluster-based retrieval using compressed cluster-skipping inverted files

3. Arash Asadpour , Rad Niazadeh , Amin Saberi , and Ali Shameli . 2022. Sequential Submodular Maximization and Applications to Ranking an Assortment of Products. Operations Research ( 2022 ). Arash Asadpour, Rad Niazadeh, Amin Saberi, and Ali Shameli. 2022. Sequential Submodular Maximization and Applications to Ranking an Assortment of Products. Operations Research (2022).

4. Pranjal Awasthi , Matthäus Kleindessner , and Jamie Morgenstern . 2020 . Equalized Odds Postprocessing under Imperfect Group Information . In International Conference on Artificial Intelligence and Statistics. PMLR, 1770–1780 . Pranjal Awasthi, Matthäus Kleindessner, and Jamie Morgenstern. 2020. Equalized Odds Postprocessing under Imperfect Group Information. In International Conference on Artificial Intelligence and Statistics. PMLR, 1770–1780.

5. Random sampling from a search engine's index