TY - GEN
T1 - A comprehensive survey of Explainable Artificial Intelligence (XAI) methods
T2 - 24th International Conference on Web Information Systems Engineering, WISE 2023
AU - Hanif, Ambreen
AU - Beheshti, Amin
AU - Benatallah, Boualem
AU - Zhang, Xuyun
AU - Habiba, null
AU - Foo, EuJin
AU - Shabani, Nasrin
AU - Shahabikargar, Maryam
PY - 2023
Y1 - 2023
N2 - Artificial Intelligence (AI) is undergoing a significant transformation. In recent years, the deployment of AI models, from Analytical to Cognitive and Generative AI, has become imminent; however, the widespread utilization of these models has prompted questions and concerns within the research and business communities regarding their transparency and interpretability. A primary challenge lies in comprehending the underlying reasoning mechanisms employed by AI-enabled systems. The absence of transparency and interpretability into the decision-making process of these systems indicates a deficiency that can have severe consequences, e.g., in domains such as medical diagnosis and financial decision-making, where valuable resources are at stake. This survey explores Explainable AI (XAI) techniques within the AI system pipeline based on existing literature. It covers tools and applications across various domains, assessing current methods and addressing challenges and opportunities, particularly in the context of Generative AI.
AB - Artificial Intelligence (AI) is undergoing a significant transformation. In recent years, the deployment of AI models, from Analytical to Cognitive and Generative AI, has become imminent; however, the widespread utilization of these models has prompted questions and concerns within the research and business communities regarding their transparency and interpretability. A primary challenge lies in comprehending the underlying reasoning mechanisms employed by AI-enabled systems. The absence of transparency and interpretability into the decision-making process of these systems indicates a deficiency that can have severe consequences, e.g., in domains such as medical diagnosis and financial decision-making, where valuable resources are at stake. This survey explores Explainable AI (XAI) techniques within the AI system pipeline based on existing literature. It covers tools and applications across various domains, assessing current methods and addressing challenges and opportunities, particularly in the context of Generative AI.
KW - Explainable Artificial Intelligence
KW - Artificial Intelligence
KW - Transparency
KW - Interpretability
UR - http://www.scopus.com/inward/record.url?scp=85175957950&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-7254-8_71
DO - 10.1007/978-981-99-7254-8_71
M3 - Conference proceeding contribution
AN - SCOPUS:85175957950
SN - 9789819972531
T3 - Lecture Notes in Computer Science
SP - 915
EP - 925
BT - Web Information Systems Engineering – WISE 2023
A2 - Zhang, Feng
A2 - Wang, Hua
A2 - Barhamgi, Mahmoud
A2 - Chen, Lu
A2 - Zhou, Rui
PB - Springer, Springer Nature
CY - Singapore
Y2 - 25 October 2023 through 27 October 2023
ER -