Summary of Nlpguard: a Framework For Mitigating the Use Of Protected Attributes by Nlp Classifiers, By Salvatore Greco et al.
NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
by Salvatore Greco, Ke Zhou, Licia Capra, Tania Cerquitelli, Daniele Quercia
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 A novel framework called NLPGuard is introduced to mitigate the reliance on protected attributes in Natural Language Processing (NLP) classifiers, addressing a critical issue in AI regulations. The traditional bias mitigation methods in NLP focus on comparable performance across different groups but fail to address the underlying problem of relying on protected attributes. NLPGuard takes an unlabeled dataset, an existing NLP classifier, and its training data as input, producing a modified training dataset that reduces dependence on protected attributes without compromising accuracy. The framework is applied to three classification tasks: toxic language identification, sentiment analysis, and occupation classification. Experimental results show that current NLP classifiers heavily depend on protected attributes, with up to 23% of the most predictive words associated with these attributes. However, NLPGuard effectively reduces this reliance by up to 79%, while slightly improving accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NLPGuard is a new tool that helps make language processing models fairer and more transparent. Right now, some AI systems are too good at telling certain things about people just because of their race, gender, or other personal characteristics. This isn’t fair or respectful. NLPGuard tries to fix this by taking an existing AI model and its training data, then creating a new version that doesn’t rely so heavily on these sensitive attributes. The team tested NLPGuard with three different tasks: identifying mean language, figuring out people’s emotions, and guessing what someone does for work. They found that the current models are really good at using protected attributes to make predictions, but NLPGuard can help reduce this reliance by up to 79% without losing accuracy. |
Keywords
» Artificial intelligence » Classification » Natural language processing » Nlp
