# FairSearch: A Tool For Fairness in Ranked Search Results

## TL;DR

FairSearch introduces an open source API that integrates fairness algorithms into search engines, enabling developers to promote equitable ranked search results and mitigate bias.

## Contribution

It provides the first open source tools and Elasticsearch plugins for implementing fairness in ranked search results using FA*IR and DELTR algorithms.

## Key findings

- Implemented FA*IR and DELTR algorithms as Python and Java libraries.
- Developed Elasticsearch plugins for easy integration of fairness algorithms.
- Facilitated fair search result implementation in existing search engine systems.

## Abstract

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.