Summary of Fairerclip: Debiasing Clip’s Zero-shot Predictions Using Functions in Rkhss, by Sepehr Dehdashtian et al.
FairerCLIP: Debiasing CLIP’s Zero-Shot Predictions using Functions in RKHSs
by Sepehr Dehdashtian, Lan Wang, Vishnu Naresh Boddeti
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Large pre-trained vision-language models like CLIP excel in various downstream zero-shot prediction tasks due to their compact and general-purpose representations of text and images. However, these models may propagate or amplify societal biases in the training data and learn to rely on spurious features. To address this issue, researchers propose FairerCLIP, a method for making zero-shot predictions with CLIP more fair and robust to spurious correlations. The approach formulates the problem of jointly debiasing CLIP’s image and text representations in reproducing kernel Hilbert spaces (RKHSs), offering flexibility, ease of optimization, and sample efficiency over existing methods. Empirically, FairerCLIP achieves significant accuracy gains on benchmark fairness and spurious correlation datasets compared to its baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how a type of artificial intelligence called CLIP can be biased or unfair in what it predicts. For example, if the training data is biased towards one group of people, then the AI might also make predictions that favor that group. To fix this problem, the researchers came up with an idea called FairerCLIP. It makes the AI more fair and less likely to rely on things that aren’t important. This helps the AI predict things more accurately and fairly. The researchers tested their method and found that it worked better than other ways of making AI predictions. |
Keywords
* Artificial intelligence * Optimization * Zero shot
