Summary of Disentangling Hate Across Target Identities, by Yiping Jin et al.
Disentangling Hate Across Target Identities
by Yiping Jin, Leo Wanner, Aneesh Moideen Koya
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 research investigates the biases and limitations of hate speech classifiers in detecting hateful expressions towards different target identities. By analyzing two recently developed test datasets for hate speech detection, the study quantifies the impact of various factors on prediction performance. The results demonstrate that popular industrial and academic models assign higher hatefulness scores based solely on the mention of specific target identities, highlighting concerns about biased predictions. Moreover, the study reveals that models often conflate hatefulness with emotional polarity, potentially misflagging posts expressing anger or disapproval as hateful themselves. The findings have worrisome implications for the effectiveness of hate speech detectors in protecting vulnerable identity groups. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hate speech detection is a challenge because machine learning models can be biased against certain groups. A new study looked at how well these models work and found that they’re not fair to everyone. The researchers tested different models on two special datasets and discovered that some models give higher scores just because of who the hate speech is directed towards. This means that if someone expresses anger or dislike towards hate speech, the model might think it’s actually hateful and try to remove it. This could make things worse for people we want to protect from hate speech. |
Keywords
» Artificial intelligence » Machine learning
