Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications
Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the d...
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| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/11/149 |
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| author | Sayda Umma Hamida Mohammad Jabed Morshed Chowdhury Narayan Ranjan Chakraborty Kamanashis Biswas Shahrab Khan Sami |
| author_facet | Sayda Umma Hamida Mohammad Jabed Morshed Chowdhury Narayan Ranjan Chakraborty Kamanashis Biswas Shahrab Khan Sami |
| author_sort | Sayda Umma Hamida |
| collection | DOAJ |
| description | Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications. |
| format | Article |
| id | doaj-art-39a759507f024b4b96b9c1dc14786d26 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-39a759507f024b4b96b9c1dc14786d262024-11-26T17:51:10ZengMDPI AGBig Data and Cognitive Computing2504-22892024-10-0181114910.3390/bdcc8110149Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and ApplicationsSayda Umma Hamida0Mohammad Jabed Morshed Chowdhury1Narayan Ranjan Chakraborty2Kamanashis Biswas3Shahrab Khan Sami4Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Shah Jalal University of Science and Technology, Sylhet 3114, BangladeshArtificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.https://www.mdpi.com/2504-2289/8/11/149artificial intelligenceexplainable AItrust in AIhealthcare AIAI interpretabilityAI transparency |
| spellingShingle | Sayda Umma Hamida Mohammad Jabed Morshed Chowdhury Narayan Ranjan Chakraborty Kamanashis Biswas Shahrab Khan Sami Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications Big Data and Cognitive Computing artificial intelligence explainable AI trust in AI healthcare AI AI interpretability AI transparency |
| title | Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications |
| title_full | Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications |
| title_fullStr | Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications |
| title_full_unstemmed | Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications |
| title_short | Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications |
| title_sort | exploring the landscape of explainable artificial intelligence xai a systematic review of techniques and applications |
| topic | artificial intelligence explainable AI trust in AI healthcare AI AI interpretability AI transparency |
| url | https://www.mdpi.com/2504-2289/8/11/149 |
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