BAdd: Bias Mitigation through Bias Addition
fairness;bias;spurious correlations
Yes
No ethics review needed.
Please see the questions in the weakness section.
- The paper is clear and well-written.

- The core idea seems interesting and is supported by various experiments.
The document introduces BAdd, a method for mitigating bias in deep learning models by injecting bias-capturing features into the training process. This approach aims to create bias-neutral representations by incorporating features that encode bias-inducing attributes, thus preventing the model from relying on these biases during training. 


The core idea is to divide the training loss between bias-aligned and bias-conflicting samples and study their behavior. To mitigate the loss spike due to underfitting on bias-aligned samples, the authors introduce the attributes themselves as features in the final layer, which mitigates the loss spikes and leads to proper training.
The idea of Bias Injection to Mitigate Bias has been used in the following work [1].

Although the idea is that a bias injection module, can prevent the loss spike. However, when the loss is forced to be zero, it needs to overcorrect the bias injection module, does it lead to correct features?

For the bias-aligned examples, the network can probably take the shortcut. Hence, the learning needs to happen just which Bias Corrected samples. In case B_c >> B_a won’t it affect the learning of diverse features? It would be great if the authors could clarify this aspect more.


The introduction section mentions that the method is more suitable for real-world. However, on closely examining the method, I found that BAdd also requires knowing the bias attributes. Could the authors please clarify on this aspect?
Yes
No ethics review needed.
please check the weakness part, and give some explanations.
1.BAdd reduces bias effectively by adding bias-related features into the training, helping the model avoid being influenced by biased data.
2.The authors showed that BAdd works well on different datasets, with consistent improvements in various bias situations, proving that the method is scalable and works in different applications.
The model proposed in this paper learns unbiased features by adding specific elements that capture potential biases during training. This ensures that the model doesn’t rely on biased information when making predictions. BAdd was tested on various datasets, including those with single and multiple biases, and showed strong improvements in reducing bias and overall model performance.
So,  this paper:
1.Introduced BAdd, an easy and effective method for learning unbiased features by incorporating bias-detecting elements into training.
2.Evaluated BAdd on several benchmarks, including different types of biased datasets, showing that it outperforms other state-of-the-art methods.
1.BAdd requires a classifier or labels that identify the bias, which may not always be available. The authors could consider ways to detect and handle biases automatically without needing predefined labels.
2.The method mainly addresses visual biases, and it’s unclear if it works for other types of biases, like those in text, limiting its use beyond visual data.
3.The paper is clear, but lacks a detailed comparison to standard deep learning training. It’s not explained how the bias-detecting classifier fits into the training process.
4.In line 728, the tense is inconsistent—“were” should be changed to “are.”
Yes
No ethics review needed.
1-Describe the key differences between the BAdd and  FLAC [1].

2-Conduct experiments using different architectures, such as ViT with BAdd, to prove that the approach works properly with different architectures. Also, use the ImageNet dataset to show the approach performance with the balanced dataset.

3-Discuss the computational complexity of BAdd approach.

4-We encourage the author to do more ablation studies on the proposed approach to show consistency, such as changing the batch size.
1-	The problem is described clearly.

2-	The investigated problem is important.
The paper introduces a BAdd approach to mitigate the effects of biased data during model training. The idea is to incorporate captured bias features into the final layer of the model, which helps the model be invariant for these features and create a bias-neutral feature representation.
1-	The BAdd approach seems similar to FLAC [1]. 

2-	On page 3, The author claims that BAdd is easily applied to any network architecture and to any CV dataset. This claim needs to be proven.

3-	The paper does not discuss the computational complexity or scalability of the proposed approach in detail, which could be a concern for large-scale applications.

4-	Limited Ablation Studies: While the paper includes some ablation studies, more extensive ablations could strengthen the claims about the individual contributions of each component of the proposed method.

[1] Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, and Christos Diou. Flac: Fairnessaware representation learning by suppressing attribute-class associations. arXiv preprint arXiv:2304.14252, 2023a.
Yes
No ethics review needed.
Availability of Protected Attributes: How does BAdd perform when protected attribute labels are unavailable or unreliable?

Handling Unknown Biases: In real-world applications where biases may be unknown or multifaceted, how can BAdd be adapted to mitigate biases that are not explicitly identified?
Versatility: The approach is architecture-agnostic and can be easily integrated into any deep learning model without extensive modifications or pre-processing.
Visualization of Results: Use of activation maps and GradCam visualizations helps to intuitively demonstrate how BAdd shifts the model's focus away from bias-inducing features.
The paper introduces BAdd (Bias Addition), a method for mitigating bias in deep learning models for computer vision tasks. Bias in datasets often arises from spurious correlations between certain attributes (e.g., gender, race, background) and target variables, leading models to make decisions based on these irrelevant features. Existing bias mitigation methods often struggle in complex, real-world scenarios, especially when multiple biases are present.

BAdd addresses this issue by injecting bias-capturing features into the model's training process. Specifically, it involves adding features that encode the protected (bias-inducing) attributes into the penultimate layer of the model during training. This approach aims to decouple the learning of biased features from the optimization process, allowing the model to learn bias-neutral representations of the data.

The method is evaluated on seven benchmarks, including both artificially biased datasets (e.g., Biased-MNIST, Corrupted-CIFAR10) and more complex, multi-attribute biased datasets (e.g., FB-Biased-MNIST, CelebA, UrbanCars). Notably, it achieves accuracy improvements of +27.5% on FB-Biased-MNIST and +5.5% on CelebA.
Assumption of Bias Representability: The method assumes that biases can be captured and represented explicitly. In cases where biases are subtle or unknown, this approach may be less effective.
Potential Increase in Model Complexity: Adding bias-capturing features might increase computational overhead during training and potentially during inference if not properly managed.
Fine-Tuning Requirement: The necessity of a fine-tuning step adds an extra phase to the training process, which might not be ideal in time-constrained or resource-limited applications.
Limited Analysis on Real-World Biases: While the paper includes evaluations on datasets like CelebA, further analysis on real-world datasets with complex, less-defined biases would strengthen the applicability of BAdd.
@inproceedings{
anonymous2024badd,
title={{BA}dd: Bias Mitigation through Bias Addition},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=fMOUybjbnO},
note={under review}
}
Computer vision (CV) datasets often exhibit biases in the form of spurious correlations between certain attributes and target variables that are perpetuated by Deep Learning (DL) models. While recent efforts aim to mitigate such biases and foster bias-neutral representations, they fail in complex real-world scenarios. In particular, existing methods excel in controlled experiments on benchmarks with single-attribute injected biases, but struggle with complex multi-attribute biases that naturally occur in established CV datasets. Here, we introduce BAdd, a simple yet effective method that allows for learning bias-neutral representations invariant to bias-inducing attributes.  It achieves this by injecting features encoding these attributes into the training process. BAdd is evaluated on seven benchmarks and exhibits competitive performance, surpassing state-of-the-art methods on both single- and multi-attribute bias settings. Notably, it achieves +27.5% and +5.5% absolute accuracy improvements on the challenging multi-attribute benchmarks, FB-Biased-MNIST and CelebA, respectively.
I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
fairness bias spurious correlations
I certify that there is no acknowledgement section in this submission for double blind review.
/pdf/eb3e24d793b47d709b90c9f3bce0e41a4cc2a5ab.pdf
alignment, fairness, safety, privacy, and societal considerations
I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
/attachment/e3f0fa9c6f73568509b09ca9042d3acbfb8a7142.zip
BAdd: Bias Mitigation through Bias Addition
ICLR 2025 Conference Submission
ICLR.cc/2025/Conference/Submission