Nasheen Nur - Academia.edu (original) (raw)
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In a wide range of industries and academic fields, artificial intelligence is becoming increasing... more In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow
in popularity and performance. Although AI models, particularly machine learning models, are
successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due
to the lack of transparency behind their behavior, users need more understanding of how these models
make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex
machine learning systems utilize less transparent algorithms, thereby exacerbating the problem.
This survey analyzes the significance and evolution of explainable AI (XAI) research across various
domains and applications. Throughout this study, a rich repository of explainability classifications
and summaries has been developed, along with their applications and practical use cases. We believe
this study will make it easier for researchers to understand all explainability methods and access
their applications simultaneously.
In a wide range of industries and academic fields, artificial intelligence is becoming increasing... more In a wide range of industries and academic fields, artificial intelligence is becoming
increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow
in popularity and performance. Although AI models, particularly machine learning models, are
successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due
to the lack of transparency behind their behavior, users need more understanding of how these models
make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex
machine learning systems utilize less transparent algorithms, thereby exacerbating the problem.
This survey analyzes the significance and evolution of explainable AI (XAI) research across various
domains and applications. Throughout this study, a rich repository of explainability classifications
and summaries has been developed, along with their applications and practical use cases. We believe
this study will make it easier for researchers to understand all explainability methods and access
their applications simultaneously.