Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
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 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.
Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Quoc Hung Cao, Van Binh Truong, Quoc Khanh Nguyen, Hung Cao
3/21/2025
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To address the challenges of providing quick and plausible explanations in Explainable AI (XAI) for object detection models, we introduce the Gaussian Class Activation Mapping Explainer (G-CAME).
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In recent years, reinforcement learning (RL) has emerged as a new, promising way to solve old problems. The algorithms’ role in finding approximate solutions in NP-hard complexity became crucial for developing modern intelligent decisions.
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LangXAI is a framework that integrates Explain able Artificial Intelligence (XAI) with advanced vi sion models to generate textual explanations for visual recognition tasks.
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