Summary of Learning to Defer in Content Moderation: the Human-ai Interplay, by Thodoris Lykouris et al.
Learning to Defer in Content Moderation: The Human-AI Interplay
by Thodoris Lykouris, Wentao Weng
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Human-Computer Interaction (cs.HC); Performance (cs.PF)
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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 Machine learning models have revolutionized content moderation on online platforms by enabling efficient collaboration with human reviewers. However, traditional heuristic-based approaches disregard crucial factors such as prediction uncertainty, time-varying human review capacity and post arrivals, and selective sampling in datasets. This paper explores innovative methods to address these limitations and improve the overall quality of content moderation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Online platforms rely on content moderation to keep users safe. Right now, this process involves a mix of AI and humans working together. The problem is that current approaches don’t take into account important factors like how uncertain AI predictions are, when humans have time to review posts, and how posts get filtered before being reviewed by humans. |
Keywords
* Artificial intelligence * Machine learning
