Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
This work addresses the problem of achieving fair and trustworthy AI models for edge camera systems, though it appears incremental as it applies existing XAI techniques to a specific domain.
The research tackled the challenge of interpreting and debugging complex AI models for human detection on edge cameras by developing a diagnostic method using Explainable AI (XAI) to identify biases, finding that the training dataset was the main source of bias and suggesting model augmentation as a solution.
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.