Summary of Inference-time Selective Debiasing to Enhance Fairness in Text Classification Models, by Gleb Kuzmin et al.
Inference-Time Selective Debiasing to Enhance Fairness in Text Classification Models
by Gleb Kuzmin, Neemesh Yadav, Ivan Smirnov, Timothy Baldwin, Artem Shelmanov
First submitted to arxiv on: 27 Jul 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 selective debiasing method is an inference-time safety mechanism designed to enhance model quality in terms of prediction performance and fairness. It draws inspiration from selective classification, where predictions with low quality are discarded or debiased instead. The approach identifies potentially biased predictions using a bias quantification method based on KL divergence, which outperforms standard uncertainty quantification methods. Experiments on text classification datasets demonstrate that selective debiasing reduces the performance gap between post-processing methods and debiasing techniques from training and pre-processing categories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re going to explain something called “selective debiasing”. It’s a way to make sure AI models don’t make unfair predictions. Sometimes, we can’t retrain the model because it would take too much time or effort. So, this method looks at how confident the model is in its prediction and removes any biases from those predictions that are not very good. This helps make the model more fair and accurate. |
Keywords
» Artificial intelligence » Classification » Inference » Text classification
