Toward Accountable and Explainable Artificial Intelligence Part one: Theory and Examples
Like other Artificial Intelligence (AI) systems, Machine Learning (ML) applications cannot explain decisions, are marred with training-caused biases, and suffer from algorithmic limitations. Their eXplainable Artificial Intelligence (XAI) capabilities are typically measured in a two-dimensional spac...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/89344 |
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