FairSearch: A Tool For Fairness in Ranked Search Results
This addresses fairness issues in online rankings that affect individuals and groups in areas like hiring and education, but it is incremental as it packages existing algorithms into tools.
The paper tackles the problem of unfairness in ranked search results by introducing FairSearch, an open source API that implements two fair ranking algorithms, FA*IR and DELTR, as libraries in Python and Java and plugins for Elasticsearch, enabling developers to integrate fairness into existing search systems.
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 career and business opportunities, educational placement, access to benefits, and even social and reproductive 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 algorithms from the fair ranking literature, namely FA*IR (Zehlike et al., 2017) and DELTR (Zehlike and Castillo, 2018) and provide them as stand-alone libraries in Python and Java. Additionally we implement interfaces to Elasticsearch for both algorithms, that use the aforementioned Java libraries and are then provided as Elasticsearch plugins. Elasticsearch is a well-known search engine API based on Apache Lucene. With our plugins we 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.