Research Paper Analysis
Do Students Debias Like Teachers? On the Distillability of Bias Mitigation Methods
Jiali Cheng, Chirag Agarwal, Hadi Amiri
Knowledge Distillation (KD), commonly used for model compression and knowledge transfer, is explored for its effect on a model's robustness against spurious correlations. This study investigates KD's ability to transfer "debiasing" capabilities from teacher models to student models in natural language inference (NLI) and image classification tasks. The findings reveal that while KD has benefits, it generally undermines debiasing capabilities, identifying critical internal mechanisms behind this behavior and proposing effective solutions.
Executive Impact: Key Takeaways for Enterprise AI
This study reveals that while knowledge distillation is effective for model compression, it often undermines the debiasing capabilities of student models. Despite efforts to transfer knowledge from robust teachers, students can become more susceptible to spurious correlations, leading to degraded performance on out-of-distribution data. Our research identifies the internal mechanisms behind this, such as divergent attention patterns, and proposes targeted solutions to enhance the distillability of bias mitigation, crucial for reliable enterprise AI deployments.
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Vanilla KD Spurious Gap (Teacher-Student)
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Max Spurious Gap Reduction (Proposed Solutions)
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Effective Strategies for Improved Debiasing
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Problem & Setup
KD Effects on Debiasing
Internal Mechanisms
Proposed Solutions
Problem Formulation & Experimental Setup
This section outlines the core investigation into Knowledge Distillation (KD)'s effect on debiasing methods. We define distillability as the performance maintained before and after distilling a debiased model, and contribution as the improvement gained by training with KD. Our experimental setup includes training teacher (fT) and student (fS) models without KD, then distilling knowledge from fT to a student model (gT->S). We compare fT vs. gT->S (C1) to assess debiasing capability transfer and fS vs. gT->S (C2) to evaluate KD's benefits. Experiments employ various backbones (BERT, T5, ResNet, ViT), datasets (CelebA, Waterbird, MNLI, QQP), and a comprehensive list of debiasing methods like ERM, HypothesisOnly-PoE, WeakLearner-PoE, KernelWhitening, AttentionPoE, σ-Damp, DeepFeatReweight, and PerSampleGrad.
Effect of Knowledge Distillation
Our findings reveal that debiasing capability is generally undermined post-KD. Teacher models consistently outperform smaller student models on ID and OOD test sets. KD, while aiming to mimic teacher logits, can increase student susceptibility to spurious correlations, especially when the teacher is not fully unbiased. Students exhibit diverse distribution shifts in predicted probabilities, with larger drops on OOD. Surprisingly, larger teachers do not guarantee more robust students; the effectiveness of transfer depends on scale similarity between teacher and student. Moreover, models trained from scratch (Non-KD) often achieve smaller spurious gaps than those trained with KD, as KD's objective of matching logits can inject spurious correlations and overconfidence, hindering generalization.
Internal Mechanisms of Distillability
To understand the observed debiasing behavior, we investigated internal mechanisms, focusing on activation patterns and circuit discovery. Activation-level analysis shows that KD successfully transfers ID knowledge between teacher and student, with earlier and mid-layers aligning. However, for OOD data, the internal representations of mid and later layers diverge significantly, explaining the performance degradation. Circuit discovery reveals that Non-KD models primarily leverage attention heads for final logits, while KD-trained models tend to emphasize MLP layers and suppress attention contributions. This suggests KD's focus on logit matching might cause it to miss crucial contributions from earlier, debiasing-relevant layers.
Proposed Solutions for Improvement
Based on our analysis, we propose three effective solutions to improve the distillability of debiasing methods:
- Data Augmentation (DA): Providing high-quality data (e.g., via Seq-Z filtering or equally represented subgroups) can significantly improve debiasing capability transfer, as spurious correlations often originate from the dataset itself.
- Iterative Knowledge Distillation (IKD): Transferring knowledge smoothly across scales (e.g., from SN to SN-1, then SN-1 to SN-2) improves effectiveness, especially when teacher and student models have similar capacities.
- Initialize Student with Teacher Weights (Init): Starting student models with teacher weights can provide a head-start, align activation spaces, and alleviate optimization challenges, though its gains might be less significant compared to DA.
These solutions collectively lead to improved distillability, with Data Augmentation showing the largest performance gains, especially in reducing spurious gaps.
4.5
% Reduction in Spurious Gap (via Data Augmentation)
Data Augmentation (DA) yields the largest improvement in distillability, reducing the spurious gap between teacher and student models by 4.5 absolute percentage points. This underscores the critical role of high-quality data in mitigating biases within enterprise AI systems.
Enterprise Process Flow: Debiasing with Knowledge Distillation
Train Teacher Model (fT) Without KD
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Train Student Model (fS) Without KD
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Apply KD to Transfer from fT to gT->S
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Compare fT vs. gT->S (RQ1)
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Compare fS vs. gT->S (RQ2)
| Metric | Teacher (fT) vs. Student (gT->S) - Vanilla KD | Non-KD (fS) vs. KD (gT->S) - Vanilla KD | Teacher (fT) vs. Student (gT->S) - With Data Augmentation |
|---|---|---|---|
| ID Performance Gap (↓) | -5.1% (Students perform worse) | -1.4% (Non-KD slightly better) | -2.3% (Improved, but still a gap) |
| OOD Performance Gap (↓) | -7.3% (Significant student degradation) | -0.7% (Non-KD performs similarly) | -5.4% (Still a notable gap) |
| Spurious Gap (↓) | 12.7% (Students much more biased) | 2.2% (Non-KD has smaller gap) | 8.2% (Significantly reduced gap) |
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Empirical Observation: The Scale Mismatch Challenge in Debiasing
Our findings reveal that the effectiveness of debiasing capability transfer through knowledge distillation is highly dependent on the scale similarity between the teacher and student models. When there is a large mismatch (e.g., a large, complex teacher distilling to a very small, simple student), the student struggles to effectively learn and may even amplify existing biases, leading to degraded performance on out-of-distribution data. Conversely, models of similar scales exhibit better knowledge transfer and higher prediction agreement. This highlights a critical need for iterative distillation or targeted curriculum-based approaches when deploying AI models of varying complexities in enterprise environments, ensuring robust and unbiased performance across the board.
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Your Journey to Debiased AI Excellence
A structured roadmap for integrating robust and debiased AI models into your enterprise, leveraging the insights from cutting-edge research.
Phase 01: Initial Assessment & Bias Audit
Conduct a thorough audit of existing datasets and models to identify potential biases and spurious correlations, defining key performance indicators for debiasing success.
Phase 02: Teacher Model Selection & Training
Select or develop robust teacher models, ensuring they are trained with advanced debiasing techniques and rigorously validated for fairness and OOD generalization.
Phase 03: Data Augmentation & Curriculum Development
Implement high-quality data augmentation strategies and design a curriculum for iterative knowledge distillation, addressing dataset vulnerabilities identified in Phase 01.
Phase 04: Student Model Distillation & Refinement
Apply iterative knowledge distillation techniques, potentially initializing student models with teacher weights, and fine-tune to optimize debiasing capabilities across varying model scales.
Phase 05: Continuous Monitoring & Adaptation
Establish continuous monitoring for bias drift and model performance, implementing feedback loops to adapt and retrain models, ensuring sustained robustness and fairness in production.
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