Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems

Alisa Rieger, Mariet Theune, Nava Tintarev

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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.
Original languageEnglish
Title of host publicationNL4XAI 20202nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence
EditorsJose Alonso, Alejandro Catala
PublisherAssociation for Computational Linguistics (ACL)
Number of pages5
Publication statusAccepted/In press - 2020
Event2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence 2020 - Virtual Workshop
Duration: 18 Dec 202018 Dec 2020
Conference number: 2

Workshop

Workshop2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence 2020
CityVirtual Workshop
Period18/12/2018/12/20

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