Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities
By Waddah Saeed, Christian Walter Peter Omlin. Knowledge-Based Systems, 2021
699 citations recorded by the source corpus.
The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that identified challenges and potential research directions in XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey for challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions in XAI and (2) challenges and research directions in XAI based on machine learning life cycle's phases: design, development, and deployment. We believe that our meta-survey contributes to XAI literature by providing a guide for future exploration in the XAI area.
Subfields: Artificial Intelligence, Health Informatics
View Paper: https://www.semanticscholar.org/paper/92259fcde062eeb232214c75e849dc5d9094599c
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In Artificial Intelligence
Citations in the corpus
- A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “A number of applications, particularly in the medical diagnosis domain, require explainable artificial intelligence (XAI) [329], where information about how the decision has been made is important.”