Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
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 t...
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| Published in | Knowledge-based systems Vol. 263; p. 110273 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
05.03.2023
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| Subjects | |
| Online Access | Get full text |
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| 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 research directions in XAI organized in two themes: (1) general challenges and research directions of XAI and (2) challenges and research directions of 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. |
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| AbstractList | 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 research directions in XAI organized in two themes: (1) general challenges and research directions of XAI and (2) challenges and research directions of 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. |
| ArticleNumber | 110273 |
| Author | Omlin, Christian Saeed, Waddah |
| Author_xml | – sequence: 1 givenname: Waddah orcidid: 0000-0002-2280-4427 surname: Saeed fullname: Saeed, Waddah email: waddah.saeed@dmu.ac.uk organization: Center for Artificial Intelligence (CAIR), University of Agder, Jon Lilletuns vei 9, Grimstad, 4879, Agder, Norway – sequence: 2 givenname: Christian surname: Omlin fullname: Omlin, Christian email: christian.omlin@uia.no organization: Center for Artificial Intelligence (CAIR), University of Agder, Jon Lilletuns vei 9, Grimstad, 4879, Agder, Norway |
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