OAK4XAI: Model Towards Out-of-Box eXplainable Artificial Intelligence for Digital Agriculture

Ngo, Quoc Hung; Kechadi, Tahar; Le-Khac, Nhien-An

doi:10.1007/978-3-031-21441-7_17

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…This approach, exemplified by the Agriculture Computing Ontology (AgriComO), aims to provide not just explanations for results but also a clear understanding of the underlying concepts, algorithms, and values used by the models. By bridging the gap between data analysis and semantic understanding, OAK4XAI empowers users in agriculture to not only trust the models' outputs but also grasp the reasoning behind them [33].…”

Section: Oak4xaimentioning

confidence: 99%

“…ClAMPs and TNTRules seek to explain model predictions; nevertheless, some constraints and difficulties might not be easily accessible [30]. Although it depends on locating optimum anchors, which may be computationally costly, OAK4XAI seeks to develop succinct and understandable anchors using game theory techniques [33]. Although explanations for highly complicated decision trees may become difficult, TreeSHAPs provide clarity on how tree-based algorithms arrive at predictions [99].…”

Section: Challengesmentioning

confidence: 99%

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Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Sharma,

Chand,

Buksh

et al. 2024

Algorithms

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This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing prominent XAI solutions based on key attributes like explanation type, model dependence, and use cases. This resource equips users to navigate the diverse XAI landscape and select the most suitable framework for their specific needs. Furthermore, the study proposes a novel framework called XAIE (eXplainable AI Evaluator) for informed decision-making in XAI adoption. This framework empowers users to assess different XAI options based on their application context objectively. This will lead to more responsible AI development by fostering transparency and trust. Finally, the research identifies the limitations and challenges associated with the existing XAI frameworks, paving the way for future advancements. By highlighting these areas, the study guides researchers and developers in enhancing the capabilities of Explainable AI.

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Section: Oak4xaimentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Sharma,

Chand,

Buksh

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Ngo et al [137] have presented OAK4XAI (model towards out-of-box explainable artificial intelligence), an XAI system for text data analysis that combines domain knowledge semantics via an ontology and knowledge map model. To describe the knowledge mined in agriculture, they developed the agriculture computer ontology (AgriComO), a wellstructured framework suitable for agriculture and computer domains.…”

Section: Smart Agriculturementioning

confidence: 99%

Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review

Prihatno

Oktian

et al. 2023

Applied Sciences

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In recent years, numerous explainable artificial intelligence (XAI) use cases have been developed, to solve numerous real problems in industrial applications while maintaining the explainability level of the used artificial intelligence (AI) models to judge their quality and potentially hold the models accountable if they become corrupted. Therefore, understanding the state-of-the-art methods, pointing out recent issues, and deriving future directions are important to drive XAI research efficiently. This paper presents a systematic literature review of local explanation techniques and their practical applications in various industrial sectors. We first establish the need for XAI in response to opaque AI models and survey different local explanation methods for industrial AI applications. The number of studies is then examined with several factors, including industry sectors, AI models, data types, and XAI-based usage and purpose. We also look at the advantages and disadvantages of local explanation methods and how well they work in practical settings. The difficulties of using local explanation techniques are also covered, including computing complexity and the trade-off between precision and interpretability. Our findings demonstrate that local explanation techniques can boost industrial AI models’ transparency and interpretability and give insightful information about them. The efficiency of these procedures must be improved, and ethical concerns about their application must be resolved. This paper contributes to the increasing knowledge of local explanation strategies and offers guidance to academics and industry professionals who want to use these methods in practical settings.

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A Decision Tree as an Explainable Artificial Intelligence Technique for Identifying Agricultural Production Predictor Variables in Mexico

Ruiz-Juárez,

Castillo-Araujo,

Orozco-del-Castillo

et al. 2023

Communications in Computer and Information Science

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OAK4XAI: Model Towards Out-of-Box eXplainable Artificial Intelligence for Digital Agriculture

Cited by 5 publications

References 10 publications

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications

Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review

A Decision Tree as an Explainable Artificial Intelligence Technique for Identifying Agricultural Production Predictor Variables in Mexico

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