Enhancing the Fairness and Performance of Edge Cameras with Explainable AI

TL;DR

This paper introduces an explainable AI-based diagnostic method to improve fairness and performance of edge camera models, identifying biases and guiding model augmentation in real-world settings.

Contribution

It presents a novel XAI-driven diagnostic approach for debugging and enhancing edge AI models, focusing on bias detection and model fairness.

Findings

Training dataset identified as main bias source

Model augmentation improved fairness and performance

Validated on real-world office Edge network

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.