FairSearch: A Tool For Fairness in Ranked Search Results
FairSearch: A Tool For Fairness in Ranked Search Results
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DOI：
10.1145/3366424.3383534
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Meike Zehlike;Tom Sühr;C. Castillo;Ivan Kitanovski
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文献类型：
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作者：
Meike Zehlike;Tom Sühr;C. Castillo;Ivan Kitanovski
排名搜索结果和推荐已经成为我们在线查找内容、产品、地点和人员的主要机制。随着招聘、选择、购买和约会越来越多地通过算法进行调节，排名可能会决定商业机会、教育、获得福利甚至社交成功。因此，询问搜索结果是否会降级，边缘化或排除弱势群体的个人或推广具有不受欢迎功能的产品具有社会和道德重要性。在本文中，我们提出了公平搜索，第一个公平的开源搜索API，以提供公平的概念排名搜索结果。我们实现了来自文献的两个众所周知的算法，即FA*IR（Zehlike等人，9)和DELTR（Zehlike and Castillo，10），并将它们作为Python和Java中的独立库提供。此外，我们为这两种算法实现了Elasticsearch的接口，这是一个基于Apache Lucene的知名搜索引擎API。这些接口使用上述Java库，并使希望确保不同风格的公平搜索结果的搜索引擎开发人员能够轻松地将DELTR和FA*IR集成到现有的Elasticsearch环境中。
Ranked search results and recommendations have become the main mechanism by which we find content, products, places, and people online. With hiring, selecting, purchasing, and dating being increasingly mediated by algorithms, rankings may determine business opportunities, education, access to benefits, and even social success. It is therefore of societal and ethical importance to ask whether search results can demote, marginalize, or exclude individuals of unprivileged groups or promote products with undesired features. In this paper we present FairSearch, the first fair open source search API to provide fairness notions in ranked search results. We implement two well-known algorithms from the literature, namely FA*IR (Zehlike et al., 9) and DELTR (Zehlike and Castillo, 10) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, a well-known search engine API based on Apache Lucene. The interfaces use the aforementioned Java libraries and enable search engine developers who wish to ensure fair search results of different styles to easily integrate DELTR and FA*IR into their existing Elasticsearch environment.