Fairness and discrimination in recommendation and retrieval
Fairness and discrimination in recommendation and retrieval
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推荐和检索的公平性和歧视性
DOI：
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发表时间：
2019
期刊：
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通讯作者：
J. Konstan
中科院分区：
文献类型：
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作者：
Michael D. Ekstrand;J. Konstan
在各种人工智能和机器学习环境中，公平性和相关问题变得越来越重要。它们也与推荐系统和信息检索等相关问题高度相关，这一点在RecSys、FAT*、SIGIR以及FATREC和FACTS-IR研讨会和TREC 2019的公平专题等特别会议上得到了证明；然而，将算法公平性结构从分类、评分甚至许多排名设置转换为推荐和其他信息访问场景并不是一项简单的任务。本教程将帮助RecSys研究人员了解算法公平性，了解概念如何从其他设置中翻译和不翻译，并介绍有关该主题的日益增长的文献。
Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into recommendation and other information access scenarios is not a straightforward task. This tutorial will help orient RecSys researchers to algorithmic fairness, understand how concepts do and do not translate from other settings, and provide an introduction to the growing literature on this topic.