Towards Trustable Explainable AI
By Alexey Ignatiev. 2020
144 citations recorded by the source corpus.
Explainable artificial intelligence (XAI) represents arguably one of the most crucial challenges being faced by the area of AI these days. Although the majority of approaches to XAI are of heuristic nature, recent work proposed the use of abductive reasoning to computing provably correct explanations for machine learning (ML) predictions. The proposed rigorous approach was shown to be useful not only for computing trustable explanations but also for validating explanations computed heuristically. It was also applied to uncover a close relationship between XAI and verification of ML models. This paper overviews the advances of the rigorous logic-based approach to XAI and argues that it is indispensable if trustable XAI is of concern.
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Peter James Stuckey
- Jinqiang Yu
- Pierre Le Bodic
- Alexey Ignatiev
- Bernardo Pereira Nunes
- Qing Wang
- Ghodai M. Abdelrahman
- Joao Marques-Silva
Citations in the corpus
- Computing Optimal Decision Sets with SAT (2020, background): “Hence the rising interest in explainable artificial intelligence (XAI) [4, 14, 16, 18, 22, 23, 26, 29, 30, 36–38, 42, 43, 48, 49, 52], including research programs [3, 24] and legislation [17, 21].”
- Knowledge Tracing: A Survey (2022, background): “This leads us to investigating eXplainable Artificial Intelligence (XAI), as seen in other research fields such as [50, 51].”