Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
University of Agder · De Montfort University
Abstract
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 have identified challenges and potential research directions of XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey of challenges and future…
Citation impact
- FWCI
- 111.59
- Percentile
- 100%
- References
- 218
Authors
2Topics & keywords
- Transparency (behavior)
- Variety (cybernetics)
- Computer science
- Management science
- Data science
- Artificial intelligence
- Knowledge management
- Engineering
- Responsible consumption and production