Kati8/ICLR_2023_2025 huggingface dataset:
authors: Yi Li
authors: Nuno Vasconcelos
reviews: 
Comments: Vision-language models (VLMs) have achieved impressive performance on various tasks but have been shown to exhibit biases due to biased training data. In this study, the authors propose a simple debiasing framework, counterfactual vision-language debiasing
(CVLD), that aims to quantify and mitigate biases in vision-language models. CVLD introduces a causal intervention module to generate counterfactual image-text pairs and use causal fairness metrics to measure the difference in model predictions between original and counterfactual distributions. The authors also propose bias-free adaptation techniques to minimize bias in pre-trained models, achieving promising results in image classification, retrieval, and captioning tasks.
Strengths: 1. The paper provides a robust framework that scales to different visual-language downstream tasks like image classification, image retrieval, and image captioning tasks.

2. The proficiency of CVLD is demonstrated in a set of fine-tuning experiments across different tasks using well-established fairness measures.

3. The paper is well-written and details the objectives and results for each of the downstream tasks separately.
Weaknesses: 1. One of the primary weaknesses of the paper is its novelty in terms of the main framework. In order to infuse fairness, Agarwal et al. [1] introduced a triplet-based objective that maximizes the agreement between the original graph and its counterfactual views. Given that CVLD follows suit and incorporates a similar framework, the novelty is limited.

2. In most cases, the counterfactual image seems noisy and is non-reflective of the counterfactual protected attribute (e.g., in Fig. 3, we don't observe women riding the rowboat). In such cases, are the counterfactual just some noisy version of the original image? How do we attribute the debiasing to a protected attribute if the quality of the counterfactuals is not good?

3. The framework is a data-extensive approach, i.e., for debiasing, it needs a counterfactual version of each image-text pair and expensive fine-tuning of VLMs for debiasing.


**References**

1. Agarwal, C., Lakkaraju, H. and Zitnik, M. Towards a unified framework for fair and stable graph representation learning. In UAI, 2021.
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: This paper attempts to unify the study of biases across vision-language problems. It attempts to create counterfactuals to swap the gender in an image-text pair using readily available tools like LLMs and image-editing methodologies. With the help of the generated counterfactuals, it is shown that the model performances improve for multiple tasks like image retrieval, image classification and image captioning.
Strengths: 1. The use of LLMs and other models like Instruct Pix2Pix is smart to generate counterfactuals.
2. The authors adopt different ways to incorporate these counterfactuals into multiple downstream tasks as it is not always possible to alter the pretraining itself.
2. Using existing VLMs on top of the original datasets along with the counterfactuals seem to help reducing the bias while also maintaining model performance.
Weaknesses: 1. Lack of novelty: The paper simply uses some state-of-the-art LLM to generate counterfactual text, and null text inversion/InstructPix2Pix to generate the counterfactual images.
2. The paper only covers gender biases - no experiments on other biases like racial/age. Biases may exist even in non-social cases (like the water-land bias in the popular Waterbirds dataset). This has not been explored.
3. No comparison with other debiasing VLM methods ([1], [2]).
4. The paper advocates generating counterfactuals for bias mitigation. However, not many sample examples are shown even in the supplementary.
5. Not all biases (for example, models are seen to learn various spurious correlations like camels can only be present in deserts, airplanes can only be in the sky, etc) are quantifiable like gender. Is generating counterfactuals the solutions for those kinds of biases too?

[1] Zhu et al., Debiased Fine-Tuning for Vision-Language Models by Prompt Regularization, AAAI 2023
[2] Chuang et al., Debiasing Vision-Language Models via Biased Prompts, arxiv 2023
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 4 excellent
Clarity: 3 good
reviews: 
Comments: This paper proposes a simple framework to debias vision-language models. First, one generates a text prompt for the target image that can guide the image editing procedure. Second, one generates a counterfactual image from the text prompt that has been edited by flipping the bias-related word (e.g., boy -> girl). Third, one fine-tunes the target VLM with the generated counterfactual images. The empirical results show that this method can be used to mitigate the gender bias of many VLMs.
Strengths: - **Soundness.** The proposed framework is very reasonably designed; it makes perfect sense that such a method will work, given access to well-performing LLMs and text-based image editors.

- **Novelty.** As far as I know, the method CVLD is novel.

- **Significance of the topic.** VLMs are now one of the core backbones of most machine learning applications, and thus having a safety guarantee on such foundation models is a very important yet understudied topic.

- **Writing.** The paper is clearly written and easy to read, despite having many typos.
Weaknesses: - **Limited Empirical Evaluation.** The proposed method has been evaluated almost exclusively on a specific type of bias---the gender bias. This is a very severe limitation for a paper which frames itself as targeting general bias in VLMs; the paper exemplifies racial bias multiple times in the text. If the authors are exclusively targeting the gender bias, a significant portion of this paper should be re-written to clarify this point.

- **Relies on external models, which may be prone to other types of bias.** The debiasing procedure of this paper relies on the generative/editing capabilities of existing models (e.g., prompt-to-prompt editing). This is a vulnerability in terms of a bias, because such edited images may be prone to other types of biases that may be difficult to detect (see, e.g., Bias-to-Text by Kim et al. (2023)). I wonder if authors could demonstrate any "robustness" of the proposed paradigm to the potential biases hidden in the LLMs or prompts.

- **(minor) Clarity.** Figure 1 is not very informative and difficult to parse what the figure is trying to say. What the sketch part is trying to say is unclear (perhaps more details in the caption will be better). Also, it took me some time to notice that "M -> F" means male -> female. The "lock" figures are somewhat difficult to tell whether they are locked or unlocked (maybe use frozen <-> fire analogy, like many other papers, or use additional color cues?).
Recommendation: 5: marginally below the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 3 good