One of our areas of interest is bias and discrimination in recommender systems. As machine learning, data mining, and other artificial intelligence techniques become increasingly pervasive in our daily lives, the research community has started to turn our attention to the question of whether they are fair. There is a variety of work happening on algorithmic fairness to try to assess whether particular systems are unfair or discriminatory, and how to mitigate this situation.

In this project, we are investigating several questions of fairness and bias in recommender systems:

  • What does it mean for a recommender to be fair, unfair, or biased?
  • What potentially discriminatory biases are present in the recommender’s input data, algorithmic structure, or output?
  • How do these biases change over time through the recommender-user feedback loop?

This is a part of our overall, ongoing goal to help make recommenders (and other AI systems) better for the people they affect.

Publications

Amifa Raj, Connor Wood, Ananda Montoly and Michael D. Ekstrand. 2020. “Comparing Fair Ranking Metrics”. To appear in Proceedings of the 3rd FAccTRec Workshop on Responsible Recommendation at 14th ACM Conference on Recommender Systems (RecSys 20).

Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, and Sebastian Kohlmeier. 2020. Overview of the TREC 2019 Fair Ranking Track. In The Twenty-Eighth Text REtrieval Conference (TREC 2019) Proceedings. arXiv:2003.11650.

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. “Exploring Author Gender in Book Rating and Recommendation”. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018).

Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, and Maria Soledad Pera. 2018. “All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness”. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (FAT* 2018).

Michael D. Ekstrand and Maria Soledad Pera. 2017. “The Demographics of Cool: Popularity and Recommender Performance for Different Groups of Users”. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017 Poster Proceedings).

Sushma Channamsetty and Michael D. Ekstrand. 2017. “Recommender Response to Diversity and Popularity Bias in User Profiles”. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference (FLAIRS 2017).