Summary of A Collaborative Content Moderation Framework For Toxicity Detection Based on Conformalized Estimates Of Annotation Disagreement, by Guillermo Villate-castillo et al.
A Collaborative Content Moderation Framework for Toxicity Detection based on Conformalized Estimates of Annotation Disagreement
by Guillermo Villate-Castillo, Javier Del Ser, Borja Sanz
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel content moderation framework that acknowledges the importance of capturing annotation disagreement when determining whether online comments are toxic. The existing approach combines human moderators with machine learning models, but often relies on data where significant disagreement occurs among annotators. Instead of dismissing this disagreement as noise, the authors interpret it as a valuable signal highlighting the ambiguity of the content. They propose a multitask learning framework that addresses annotation disagreement as an auxiliary task and incorporates uncertainty estimation techniques from Conformal Prediction. This approach allows moderators to adjust thresholds for annotation disagreement, offering flexibility in determining when ambiguity should trigger a review. The joint approach enhances model performance, calibration, and uncertainty estimation while improving the review process compared to single-task methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to better moderate online comments so that they are not too mean or offensive. Right now, people use computers to help with this task, but these computers can make mistakes because humans don’t always agree on what is and isn’t toxic. The authors of the paper think that instead of ignoring these disagreements, we should use them to improve our moderation systems. They propose a new way to do this by using special computer techniques that take into account both the human agreement and the computer’s uncertainty about whether something is toxic or not. This approach helps computers make better decisions and gives moderators more flexibility in deciding what to do with comments. |
Keywords
» Artificial intelligence » Machine learning
