Summary of Learning to Complement and to Defer to Multiple Users, by Zheng Zhang et al.
Learning to Complement and to Defer to Multiple Users
by Zheng Zhang, Wenjie Ai, Kevin Wells, David Rosewarne, Thanh-Toan Do, Gustavo Carneiro
First submitted to arxiv on: 9 Jul 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 paper introduces a novel methodology for Human-AI Collaboration in Classification (HAI-CC), called Learning to Complement and to Defer to Multiple Users (LECODU). This approach combines learning to complement, where AI collaborates with users, and learning to defer, where AI defers to users. The LECODU methodology maximizes classification accuracy and minimizes collaboration costs associated with user involvement. The paper presents comprehensive evaluations across real-world and synthesized datasets, demonstrating the superior performance of LECODU compared to state-of-the-art HAI-CC methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research develops a new way for humans and AI to work together on tasks like classifying objects. Right now, this process is complex and hasn’t been studied in a unified system. The team creates a new method called Learning to Complement and to Defer to Multiple Users (LECODU). This approach combines two ways of working with users: one where AI helps humans make decisions, and another where AI asks humans for help. LECODU also figures out the best number of people to involve in the decision-making process. The results show that this new method is better than current approaches at classifying objects, even when some users give incorrect information. |
Keywords
» Artificial intelligence » Classification
