Fairness and Discrimination in Retrieval and Recommendation

@inproceedings{Ekstrand2019FairnessAD,
  title={Fairness and Discrimination in Retrieval and Recommendation},
  author={Michael D. Ekstrand and Robin D. Burke and Fernando D{\'i}az},
  booktitle={SIGIR},
  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 information retrieval and related problems such as recommendation, as evidenced by the growing literature in SIGIR, FAT*, RecSys, and special sessions such as the FATREC workshop and the Fairness track at TREC 2019; however, translating algorithmic fairness constructs from classification, scoring, and even many ranking settings into information… CONTINUE READING

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