Enhancing the Fairness and Performance of Edge Cameras with Explainable AI | |
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Author | |
Abstract |
The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using XAI for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models. |
Year of Publication |
2024
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Date Published |
jan
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URL |
https://ieeexplore.ieee.org/document/10444383
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DOI |
10.1109/ICCE59016.2024.10444383
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Google Scholar | BibTeX | DOI |