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Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models

arXiv:2603.05582v1 Announce Type: new Abstract: The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulation. However, an intriguing question arises: is it possible to extract fair and bias-agnostic subnetworks from standard vanilla-trained models without relying on additional data, such as unbiased training set? In this work, we introduce Bias-Invariant Subnetwork Extraction (BISE), a learning strategy that identifies and isolates "bias-free" subnetworks that already exist within conventionally trained models, without retraining or finetuning the original parameters. Our approach demonstrates that such subnetworks can be extracted via pruning and can operate without modification, effectively relying less on biased features and maintaining robust performance. Our findings contribute towards efficient bias mitigation through structural adaptation of pre-trained neur

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Ivan Luiz De Moura Matos, Abdel Djalil Sad Saoud, Ekaterina Iakovleva, Vito Paolo Pastore, Enzo Tartaglione · · 1 min read · 13 views
#cs.LG #cs.CV

arXiv:2603.05582v1 Announce Type: new Abstract: The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulation. However, an intriguing question arises: is it possible to extract fair and bias-agnostic subnetworks from standard vanilla-trained models without relying on additional data, such as unbiased training set? In this work, we introduce Bias-Invariant Subnetwork Extraction (BISE), a learning strategy that identifies and isolates "bias-free" subnetworks that already exist within conventionally trained models, without retraining or finetuning the original parameters. Our approach demonstrates that such subnetworks can be extracted via pruning and can operate without modification, effectively relying less on biased features and maintaining robust performance. Our findings contribute towards efficient bias mitigation through structural adaptation of pre-trained neural networks via parameter removal, as opposed to costly strategies that are either data-centric or involve (re)training all model parameters. Extensive experiments on common benchmarks show the advantages of our approach in terms of the performance and computational efficiency of the resulting debiased model.

Executive Summary

The article 'Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models' explores the concept of debiasing deep learning models without retraining or fine-tuning. The authors propose the Bias-Invariant Subnetwork Extraction (BISE) approach, which identifies and isolates 'bias-free' subnetworks within conventionally trained models through pruning. This method demonstrates robust performance while reducing reliance on biased features. The findings contribute to efficient bias mitigation through structural adaptation of pre-trained neural networks. The article presents extensive experiments on common benchmarks, showcasing the advantages of BISE in terms of performance and computational efficiency. This work has significant implications for the field of artificial intelligence and deep learning, as it addresses the pressing issue of algorithmic biases.

Key Points

  • ▸ Introduction of the Bias-Invariant Subnetwork Extraction (BISE) approach, a novel method for debiasing deep learning models
  • ▸ BISE identifies and isolates 'bias-free' subnetworks within conventionally trained models through pruning
  • ▸ The method demonstrates robust performance while reducing reliance on biased features

Merits

Strength

The BISE approach is a significant contribution to the field, as it addresses the issue of algorithmic biases without requiring additional data or costly retraining procedures.

Robust Performance

The method demonstrates robust performance on common benchmarks, indicating its potential for real-world applications.

Efficient Bias Mitigation

BISE achieves efficient bias mitigation through structural adaptation of pre-trained neural networks, reducing the computational burden of debiasing.

Demerits

Limitation

The approach relies on pruning, which may not be feasible for all models, particularly those with complex architectures or large parameter spaces.

Scalability

The method's scalability is not extensively explored, raising concerns about its applicability to large-scale deep learning models.

Interpretability

The identification of 'bias-free' subnetworks may require additional interpretability techniques to ensure that the subnetworks are indeed unbiased and not just a byproduct of the pruning process.

Expert Commentary

The article 'Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models' is a significant contribution to the field of artificial intelligence and deep learning. The authors' proposal of the BISE approach addresses the pressing issue of algorithmic biases in deep learning models, offering a novel solution that does not rely on additional data or costly retraining procedures. While the approach has its limitations, particularly in terms of scalability and interpretability, it demonstrates robust performance and efficient bias mitigation. The article's findings have significant implications for both practical and policy-related considerations, making it a valuable addition to the field.

Recommendations

  • ✓ Further research is needed to explore the scalability and interpretability of the BISE approach, particularly in the context of large-scale deep learning models.
  • ✓ The authors' proposal of the BISE approach highlights the potential benefits of neural network pruning in the context of bias mitigation, making it an area worthy of further exploration.

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