Fairness and discrimination in recommendation and retrieval

@inproceedings{Ekstrand2019FairnessAD,
  title={Fairness and discrimination in recommendation and retrieval},
  author={Michael D. Ekstrand and Robin D. Burke and Fernando D{\'i}az},
  booktitle={RecSys},
  year={2019}
}
Fairness and related concerns have become of increasing importance in a variety of AI and machine learning contexts. They are also highly relevant to recommender systems and related problems such as information retrieval, as evidenced by the growing literature in RecSys, FAT*, SIGIR, and special sessions such as the FATREC and FACTS-IR workshops and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings… CONTINUE READING