The paper "FairerCLIP: Debiasing CLIP's Zero-Shot Predictions Using Functions in RKHSSs" addresses the issue of bias and spurious correlations in zero-shot predictions made by large pre-trained vision-language models like CLIP. The authors propose FairerCLIP, a general approach that aims to make these predictions more fair and robust to spurious correlations. The key contributions of the paper include: 1. **Flexibility**: FairerCLIP can be adapted to both supervised and unsupervised learning scenarios. 2. **Ease of Optimization**: It employs an alternating optimization algorithm with closed-form solvers, leading to faster training compared to existing methods. 3. **Sample Efficiency**: Under limited data conditions, FairerCLIP outperforms baselines that fail in such scenarios. 4. **Performance**: Empirical results show significant accuracy gains on benchmark fairness and spurious correlation datasets. The paper formulates the debiasing problem in reproducing kernel Hilbert spaces (RKHSSs), which allows for a flexible and efficient approach. The method uses a non-parametric measure of statistical dependence to account for both linear and nonlinear dependencies between the debiased representation and the sensitive attribute. The optimization problem is solved through an alternating algorithm, which alternates between optimizing the image and text encoders while maintaining alignment between the representations. Experimental evaluations on various datasets, including Waterbirds, CelebA, FairFace, and the Chicago Face Database, demonstrate the effectiveness of FairerCLIP in mitigating both spurious correlations and intrinsic dependencies. The results show that FairerCLIP achieves significant improvements in fairness metrics and maintains high classification accuracy. Additionally, the paper includes ablation studies to validate the effectiveness of different components of the approach.The paper "FairerCLIP: Debiasing CLIP's Zero-Shot Predictions Using Functions in RKHSSs" addresses the issue of bias and spurious correlations in zero-shot predictions made by large pre-trained vision-language models like CLIP. The authors propose FairerCLIP, a general approach that aims to make these predictions more fair and robust to spurious correlations. The key contributions of the paper include: 1. **Flexibility**: FairerCLIP can be adapted to both supervised and unsupervised learning scenarios. 2. **Ease of Optimization**: It employs an alternating optimization algorithm with closed-form solvers, leading to faster training compared to existing methods. 3. **Sample Efficiency**: Under limited data conditions, FairerCLIP outperforms baselines that fail in such scenarios. 4. **Performance**: Empirical results show significant accuracy gains on benchmark fairness and spurious correlation datasets. The paper formulates the debiasing problem in reproducing kernel Hilbert spaces (RKHSSs), which allows for a flexible and efficient approach. The method uses a non-parametric measure of statistical dependence to account for both linear and nonlinear dependencies between the debiased representation and the sensitive attribute. The optimization problem is solved through an alternating algorithm, which alternates between optimizing the image and text encoders while maintaining alignment between the representations. Experimental evaluations on various datasets, including Waterbirds, CelebA, FairFace, and the Chicago Face Database, demonstrate the effectiveness of FairerCLIP in mitigating both spurious correlations and intrinsic dependencies. The results show that FairerCLIP achieves significant improvements in fairness metrics and maintains high classification accuracy. Additionally, the paper includes ablation studies to validate the effectiveness of different components of the approach.