Debiasing Vision-Language Models via Biased Prompts

Abstract: Machine learning models have been shown to inherit biases from their training datasets. This can be particularly problematic for vision-language foundation models trained on uncurated datasets scraped from the internet. The biases can be amplified and propagated to downstream applications like zero-shot classifiers and text-to-image generative models. In this study, we propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. In particular, we show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models. The proposed closed-form solution enables easy integration into large-scale pipelines, and empirical results demonstrate that our approach effectively reduces social bias and spurious correlation in both discriminative and generative vision-LLMs without the need for additional data or training.

• The paper introduces a calibration technique that projects out biased directions from text embeddings to mitigate spurious correlations.
• It leverages biased prompts to construct projection matrices without extra training data, leading to improved fairness on benchmarks like Waterbird and CelebA.
• Empirical tests demonstrate enhanced group robustness and diverse, fair outcomes in both zero-shot classifiers and text-to-image generative models.

Debiasing Vision-LLMs via Biased Prompts

The study presented in this paper explores the pressing challenge of biases in vision-LLMs, particularly those trained on vast, uncurated datasets sourced from the internet. Such biases, when embedded in models, are likely to propagate through downstream applications like zero-shot classifiers and text-to-image generative models, potentially resulting in unfair and inaccurate predictions. The authors propose a methodical approach to mitigative debiasing, centered around the calibration of text embeddings using projection matrices specific to biased prompts.

In understanding the mechanics, the paper highlights the limitations inherent in current vision-LLMs such as CLIP, DALLE-2, and Stable Diffusion which, despite their elite performance, often perpetuate biases regarding gender, race, and other spurious correlations present in their training sets. The study tactfully integrates the concept of projecting out biased directions within text embeddings, where the authors innovate upon earlier methods like those used for debiasing word embeddings.

The cornerstone of this method lies in defining biased subspaces using biased prompts and constructing a projection matrix. A notable element is the calibration loss which minimizes discrepancies between projected embeddings of prompts differing in spurious attributes yet retaining the same class semantics—an approach that has shown empirical effectiveness without requiring supplementary datasets or training.

Noteworthy is the capability of the calibrated projection matrix to enhance group robustness, demonstrated on benchmarks such as Waterbird and CelebA datasets. The experiments showcase how the application of the calibrated projection matrix not only improves the fairness of zero-shot models but also enriches the diversity in generated images from text-to-image models. The calibrated projection matrix significantly reduces biases without altering model parameters, signaling a leap toward computational efficiency.

Moreover, the paper discusses extending the approach to generative models, addressing inherent challenges due to their distinct nature from zero-shot classification. The methodology involves developing a calibration matrix universally applicable across various prompts, establishing calibration using prompt pairs that describe spurious features as a preprocessing stage.

Empirical evidence compounds the theoretical implications, demonstrating reduced social bias and spurious correlations in zeroshot and generative models alike. The findings suggest that debiasing textual embeddings alone can suffice to yield robust classifiers and fair generative results.

The implications for the field of AI are substantial and multifaceted. This work embarks on enhancing the fairness and accuracy of AI systems. Practically, it offers a scalable, training-free debiasing method readily adaptable to large-scale AI pipelines, thus setting a precedent for efficient bias mitigation. Theoretically, it opens avenues for further exploration into text embeddings as robust carriers of semantic integrity, essential in distinguishing target and spurious attributes.

Future work might explore refining the calibration matrix, exploring its potential across other foundational models, or extending its application to yet broader spectrum spurious correlations. Furthermore, it prompts an ethical and philosophical dialogue within AI research about constructively addressing biases to foster inclusivity and equity in machine learning applications.

In conclusion, this paper charts a promising trajectory for debiasing vision-LLMs using biased prompts—an earnest step toward equitable AI systems, fostering both technical advancement and ethical coherence.