Explainable Artificial Intelligence: a Systematic Review
- URL: http://arxiv.org/abs/2006.00093v4
- Date: Mon, 12 Oct 2020 16:16:40 GMT
- Title: Explainable Artificial Intelligence: a Systematic Review
- Authors: Giulia Vilone and Luca Longo
- Abstract summary: Machine learning has led to the development of highly accurate models but lack explainability and interpretability.
A plethora of methods to tackle this problem have been proposed, developed and tested.
This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system.
- Score: 2.741266294612776
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Explainable Artificial Intelligence (XAI) has experienced a significant
growth over the last few years. This is due to the widespread application of
machine learning, particularly deep learning, that has led to the development
of highly accurate models but lack explainability and interpretability. A
plethora of methods to tackle this problem have been proposed, developed and
tested. This systematic review contributes to the body of knowledge by
clustering these methods with a hierarchical classification system with four
main clusters: review articles, theories and notions, methods and their
evaluation. It also summarises the state-of-the-art in XAI and recommends
future research directions.
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