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.- Anthology ID:
- 2020.nl4xai-1.11
- Volume:
- 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence
- Month:
- November
- Year:
- 2020
- Address:
- Dublin, Ireland
- Editors:
- Jose M. Alonso, Alejandro Catala
- Venue:
- NL4XAI
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 50–54
- Language:
- URL:
- https://aclanthology.org/2020.nl4xai-1.11
- DOI:
- Cite (ACL):
- Alisa Rieger, Mariët Theune, and Nava Tintarev. 2020. Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems. In 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence, pages 50–54, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems (Rieger et al., NL4XAI 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.nl4xai-1.11.pdf