New tools for fair ranking available

With the support of a Data Transparency Lab grant, working with Meike Zehlike and Tom Sühr from TU Berlin, and Ivan Kitanovski from Ss. Cyril and Methodius University of Skopje, we have produced new tools for creating fair rankings.

Reference for both tools:
Meike Zehlike, Tom Sühr, Carlos Castillo, Ivan Kitanovski: "FairSearch: A Tool For Fairness in Ranked Search Results". arXiv:1905.13134 (2019). Homepage: https://github.com/fair-search

FA*IR: fair ranking by post-processing

The first set of tools correspond to the FA*IR paper in CIKM 2017, which describes a method for ranking post-processing based on a statistical test called the ranking group fairness condition:

• FA*IR library in Python
• FA*IR library in Java
• FA*IR plug-in for Elasticsearch

Reference for the FA*IR algorithm:
Meike Zehlike, Francesco Bonchi, Carlos Castillo, Sara Hajian, Mohamed Megahed, Ricardo Baeza-Yates: "FA*IR: A Fair Top-k Ranking Algorithm". Proc. of the 2017 ACM on Conference on Information and Knowledge Management (CIKM).

DELTR: fair ranking in-processing by learning-to-rank

The second set of tools correspond to an unpublished work on Learning To Rank (LTR) while reducing disparate impact, an in-processing algorithm named DELTR:

• DELTR library in Python
• DELTR library in Java
• DELTR adapter for Elasticsearch

Reference for the DELTR algorithm:
Meike Zehlike, Gina-Theresa Diehn, Carlos Castillo. "Reducing Disparate Exposure in Ranking: A Learning to Rank Approach" arXiv:1805.08716 (2018).