  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.
