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

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Title: Bayesian Case-Exclusion and Explainable AI (XAI) for Sustainable Farming
Authors: Kenny, Eoin M.Ruelle, ElodieGeoghegan, AnneTemraz, MohammedKeane, 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 systemsAgricultureSustainabilityArtificial 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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