What question did this study set out to answer?

To synthesize core concepts, programming frameworks, and development phases within explainable artificial intelligence to assist stakeholders in deploying transparent models.

September 4, 2022ACM Computing SurveysOpen Access

Explainable AI (XAI): Core Ideas, Techniques, and Solutions

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Authors

RDRudresh DwivediNetaji Subhas University of Technology DDDevam DavePandit Deendayal Petroleum University HNHet NaikAhmedabad University

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Implication

Review classifies explainable artificial intelligence techniques and frameworks across machine learning workflows, highlighting practical toolkits for transparent system deployment.

Key Points

To synthesize core concepts, programming frameworks, and development phases within explainable artificial intelligence to assist stakeholders in deploying transparent models.
Surveyed state-of-the-art programming techniques and software toolkits across the machine learning development lifecycle.
Developed a taxonomy to classify explainable AI approaches and mapped theoretical methodologies to concrete software implementations.
Established a comparative taxonomy delineating core differences among existing explainable AI techniques across distinct development phases.
Categorized practical programming frameworks and software toolkits to guide the implementation of transparent AI applications.

Cite This Study

Dwivedi et al. (2022) studied this question.

synapsesocial.com/papers/69c414b4b78463c71097f552 https://doi.org/10.1145/3561048

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Implication

Review classifies explainable artificial intelligence techniques and frameworks across machine learning workflows, highlighting practical toolkits for transparent system deployment.

Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Discussion

Member takes

Implication

Key Points

Cite This Study

Explainable AI (XAI): Core Ideas, Techniques, and Solutions

Discussion

Member takes

Implication

Key Points

Cite This Study

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