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arXiv · 2401.09852

Enhancing the Fairness and Performance of Edge Cameras with Explainable AI

Truong Thanh Hung Nguyen·Vo Thanh Khang Nguyen·Quoc Hung Cao·Van Binh Truong·Quoc Khanh Nguyen·Hung Cao
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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 Explainable AI (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.

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Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Quoc Hung Cao, Van Binh Truong, Quoc Khanh Nguyen, Hung Cao. 2024-01-18. Enhancing the Fairness and Performance of Edge Cameras with Explainable AI. https://arxiv.org/abs/2401.09852

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