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  5. Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
 
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Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming

Author(s)
Kenny, Eoin M.  
Ruelle, Elodie  
Geoghegan, Anne  
Temraz, Mohammed  
Keane, Mark T.  
et al.  
Uri
http://hdl.handle.net/10197/12206
Date Issued
2021-01-15
Date Available
2021-05-26T11:01:59Z
Abstract
Smart agriculture (SmartAg) has emerged as a rich domain for AI-driven decision support systems (DSS); however, it is often challenged by user-adoption issues. This paper reports a case-based reasoning system, PBI-CBR, that predicts grass growth for dairy farmers, that combines predictive accuracy and explanations to improve user adoption. PBI-CBR’s key novelty is its use of Bayesian methods for case-base maintenance in a regression domain. Experiments report the tradeoff between predictive accuracy and explanatory capability for different variants of PBI-CBR, and how updating Bayesian priors each year improves performance.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Subjects

Recommender systems

Agriculture

Sustainability

Artificial intelligen...

Web versions
https://ijcai20.org/
Language
English
Status of Item
Peer reviewed
Conference Details
The 29th International Joint Conference on Artificial Intelligence - 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI-20), Yokohama, Japan, January 2021 (Conference postponed due to COVID-19 pandemic)
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
No Thumbnail Available
Name

insight_publication.pdf

Size

856.23 KB

Format

Adobe PDF

Checksum (MD5)

26d3edeec68378a2477d9b76818e6bfa

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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