Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
|Title:||Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming||Authors:||Kenny, Eoin M.; Ruelle, Elodie; Geoghegan, Anne; Temraz, Mohammed; Keane, Mark T.; et al.||Permanent link:||http://hdl.handle.net/10197/12206||Date:||15-Jan-2021||Online since:||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.||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre||Type of material:||Conference Publication||Keywords:||Recommender systems; Agriculture; Sustainability; Artificial intelligence||Other versions:||https://ijcai20.org/||Language:||en||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/|
|Appears in Collections:||Insight Research Collection|
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