Fairness and Discrimination in Retrieval and Recommendation

@article{Ekstrand2019FairnessAD,
  title={Fairness and Discrimination in Retrieval and Recommendation},
  author={Michael D. Ekstrand and R. Burke and F. D{\'i}az},
  journal={Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2019}
}
  • Michael D. Ekstrand, R. Burke, F. Díaz
  • Published 2019
  • Computer Science
  • Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
  • 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
    6 Citations

    Topics from this paper

    A Conceptual Framework for Evaluating Fairness in Search
    • 1
    • PDF
    Fair ranking in academic search - Notebook for the TREC 2019 Fair Ranking Track
    • PDF
    Towards More Impactful Recommender Systems Research
    • 4
    • PDF
    Interactive and Context-Aware Systems in Tourism
    • 1
    • PDF