@inproceedings{rieger-etal-2020-toward,
    title = "Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems",
    author = {Rieger, Alisa  and
      Theune, Mari{\"e}t  and
      Tintarev, Nava},
    editor = "Alonso, Jose M.  and
      Catala, Alejandro",
    booktitle = "2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence",
    month = nov,
    year = "2020",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.nl4xai-1.11/",
    pages = "50--54",
    abstract = "Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control."
}