Summary of Enhancing the Fairness and Performance Of Edge Cameras with Explainable Ai, by Truong Thanh Hung Nguyen et al.
Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
by Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Quoc Hung Cao, Van Binh Truong, Quoc Khanh Nguyen, Hung Cao
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed diagnostic method utilizes Explainable AI (XAI) to debug complex AI models used in human detection on Edge camera systems. The approach involves expert-driven problem identification and solution creation, which was validated on the Bytetrack model in a real-world office Edge network. The study reveals that training dataset biases are the primary issue and recommends model augmentation as a solution. This research aims to develop fair and trustworthy AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI experts created a new way to fix problems with complex AI models used for detecting people on camera systems. They tested it on a real office building’s cameras and found that most issues come from how the data was trained. To solve this, they suggest making the model better by adding more training data. This helps create fair and trustworthy AI. |
