Abstract
In order to increase trust in the usage ofBayesian networks and to cement their roleas a model which can aid in critical deci-sion making, the challenge of explainabilitymust be faced. Previous attempts at explain-ing Bayesian networks have largely focusedon graphical or visual aids. In this paper weaim to highlight the importance of a naturallanguage approach to explanation and to dis-cuss some of the previous and state of the artattempts of the textual explanation of BayesianNetworks. We outline several challenges thatremain to be addressed in the generation andvalidation of natural language explanations ofBayesian Networks. This can serve as a re-search agenda for future work on natural lan-guage explanations of Bayesian Networks.
| Original language | English |
|---|---|
| Title of host publication | 2nd Workshop on Interactive Natural Language Technologyfor Explainable Artificial Intelligence |
| Subtitle of host publication | Proceedings of NL4XAI |
| Publisher | The Association for Computational Linguistics |
| Pages | 28-33 |
| Number of pages | 6 |
| ISBN (Print) | 978-1-952148-56-9 |
| Publication status | Published - 18 Dec 2020 |
| Event | 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence - Duration: 18 Dec 2020 → 18 Dec 2020 https://sites.google.com/view/nl4xai2020/program |
Workshop
| Workshop | 2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence |
|---|---|
| Period | 18/12/20 → 18/12/20 |
| Internet address |
Bibliographical note
Acknowledgments:This project has received funding from the Eu-ropean Union’s Horizon 2020 research and inno-vation programme under the Marie Skłodowska-Curie Grant Agreement No. 860621. It was also funded by the Spanish Ministry for Science, In-novation and Universities, the Galician Ministryof Education, University and Professional Train-ing and the European Regional Development Fund(grants TIN2017-84796-C2-1-R, ED431C2018/29and ED431G2019/04)
Access to Document
- Conor_et_al_NL4XAI_ExplainingBayesianNetworks_VoR
ACL materials are Copyright © 1963–2021 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/