Abstract
This paper addresses the unique challenges associated with uncertainty quantification in AI models when applied to patient-facing contexts within healthcare. Unlike traditional eXplainable Artificial Intelligence (XAI) methods tailored for model developers or domain experts, additional considerations of communicating in natural language, its presentation and evaluating understandability are necessary. We identify the challenges in communication model performance, confidence, reasoning and unknown knowns using natural language in the context of risk prediction. We propose a design aimed at addressing these challenges, focusing on the specific application of in-vitro fertilisation outcome prediction.- Anthology ID:
- 2024.uncertainlp-1.13
- Volume:
- Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St Julians, Malta
- Editors:
- Raúl Vázquez, Hande Celikkanat, Dennis Ulmer, Jörg Tiedemann, Swabha Swayamdipta, Wilker Aziz, Barbara Plank, Joris Baan, Marie-Catherine de Marneffe
- Venues:
- UncertaiNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 127–132
- Language:
- URL:
- https://aclanthology.org/2024.uncertainlp-1.13
- DOI:
- Cite (ACL):
- Adarsa Sivaprasad and Ehud Reiter. 2024. Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models. In Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024), pages 127–132, St Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models (Sivaprasad & Reiter, UncertaiNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2024.uncertainlp-1.13.pdf