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Algorithmic decisions in critical domains such as hiring, college admissions, and lending are often based on rankings.
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Evaluating and aggregating feature-based model explanations. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (Yokohama, Yokohama, Japan) (IJCAI’20) . Article 417, 7 pages
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Rank-LIME: Local Model-Agnostic Feature Attribution for Learning to Rank. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval (Taipei, Taiwan) (ICTIR ’23) . Association for Computing Machinery, New York, NY, USA, 33–37
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Joao Fonseca Venetia Pliatsika. 2023 · 2023
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A Human-in-the-loop Workflow for Multi-Factorial Sensitivity Analysis of Algorithmic Rankers. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA 2023, Seattle, WA, USA, 18 June 2023 . ACM, 5:1–5:5
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RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank
Tanya Chowdhury, Yair Zick, and James Allan. 2024 · 2024
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RankingSHAP – Listwise Feature Attribution Explanations for Ranking Models
Maria Heuss, Maarten de Rijke, and Avishek Anand. 2024 · 2024
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