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.
@article{arxiv.2401.09852,
title = {Enhancing the Fairness and Performance of Edge Cameras with Explainable AI},
author = {Truong Thanh Hung Nguyen and Vo Thanh Khang Nguyen and Quoc Hung Cao and Van Binh Truong and Quoc Khanh Nguyen and Hung Cao},
journal= {arXiv preprint arXiv:2401.09852},
year = {2024}
}