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
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.