תובנה - Machine Learning - # Disparate Impact Mitigation in Model Pruning

Mitigating Disparate Impact of Pruning in Sparse Models

Q: How can we address the generalization challenges observed in mitigating disparate impact on unseen data

To address the generalization challenges observed in mitigating disparate impact on unseen data, we can explore several strategies: Regularization Techniques: Introducing regularization terms that penalize large deviations in accuracy gaps between sub-groups during training can help improve generalization to unseen data. Cross-Validation: Implementing cross-validation techniques can provide a more robust evaluation of model performance across different subsets of the data, helping to identify and mitigate disparities that may arise during deployment. Transfer Learning: Leveraging transfer learning approaches by fine-tuning models on diverse datasets with varying distributions of protected attributes can enhance the model's ability to generalize well to new and unseen data while maintaining fairness constraints. Data Augmentation: Incorporating data augmentation techniques specifically designed to enhance representation from under-represented groups can help improve model performance and reduce disparities when faced with unseen instances. Ensemble Methods: Utilizing ensemble methods where multiple models are trained independently and then combined for predictions can help mitigate biases present in individual models, leading to improved generalization across diverse sub-groups.

Q: What are some potential implications of deploying pruned deep learning models without considering disparate impact mitigation

Deploying pruned deep learning models without considering disparate impact mitigation could have several potential implications: Reinforcement of Biases: Pruned models may inadvertently reinforce existing biases present in the training data, leading to unfair treatment or discrimination against certain demographic groups. Ethical Concerns: Deploying biased models without mitigation measures could result in unethical decision-making processes that disproportionately affect marginalized communities or individuals based on sensitive attributes like race or gender. Legal Ramifications: Failure to address disparate impact when deploying pruned models could lead to legal challenges related to discriminatory practices or violations of anti-discrimination laws in various jurisdictions. Loss of Trust and Reputation Damage: Using biased models without fairness considerations may erode trust among users, stakeholders, and regulatory bodies, potentially damaging an organization's reputation and credibility.

Q: How can we extend our approach to incorporate additional fairness notions beyond accuracy gaps

To extend our approach beyond accuracy gaps and incorporate additional fairness notions: 1.Fairness Constraints Integration: We can integrate constraints based on other fairness notions such as demographic parity, equal opportunity, or equalized odds into our optimization framework alongside accuracy gap constraints. 2Multi-Objective Optimization: Formulating a multi-objective optimization problem where disparity reduction is one objective along with other fairness metrics allows us to balance different aspects of fairness simultaneously. 3Intersectional Fairness Consideration: Extending our approach to consider intersectional groups by incorporating multiple protected attributes (e.g., race AND gender) would enable a more nuanced understanding of disparities across complex intersections. 4Adversarial Training: Employing adversarial training techniques where an adversary network aims at increasing disparity while the main network minimizes it helps create fairer representations by explicitly addressing bias within the model architecture. 5Post-Hoc Analysis: Conducting post-hoc analysis using tools like counterfactual explanations or causal inference methods enables us to understand how decisions made by the model affect different sub-groups' outcomes beyond just accuracy metrics alone.

מושגי ליבה

Our approach directly addresses the disparate impact of pruning by bounding group-level accuracy gaps between dense and sparse models, offering interpretable constraints and algorithmic accountability.

תקציר

The paper explores mitigating the disparate impact of pruning in sparse models. It introduces a constrained optimization approach that focuses on accuracy gaps between dense and sparse models to reduce systemic biases. Experimental results show reliable mitigation on training data but challenges with generalization to unseen data.
The study uses datasets like FairFace and UTKFace, highlighting the importance of ethical data sourcing and fairness considerations. The proposed method scales reliably to tasks with hundreds of sub-groups, showcasing an accountable and interpretable solution for reducing disparity induced by pruning.
Key points include the formulation of constrained excess accuracy gaps, optimization techniques for non-differentiable constraints, and the use of replay buffers to improve stability in constraint estimation. The study emphasizes the trade-off between accuracy and disparity mitigation while addressing challenges in generalization across different methods.

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arxiv.org

סטטיסטיקה

CEAG achieves a maxg ψg within the prescribed threshold at 99% sparsity.
CEAG consistently achieves a maxg ψg within the threshold across different sparsity levels.
CEAG attains feasible models in training with small degradation compared to NFT.
CEAG reliably yields models within requested disparity levels with smallest variance metrics.
CEAG reduces disparity while achieving comparable aggregate performance to NFT.

ציטוטים

"Our approach directly addresses the disparate impact of pruning by bounding group-level accuracy gaps between dense and sparse models."
"Experimental results demonstrate reliable mitigation on training data but challenges with generalization to unseen data."
"The proposed method scales reliably to tasks with hundreds of sub-groups, showcasing an accountable and interpretable solution for reducing disparity induced by pruning."

תובנות מפתח מזוקקות מ:

Balancing Act

by Meraj Hashem... ב- arxiv.org 03-11-2024

https://arxiv.org/pdf/2310.20673.pdf

שאלות מעמיקות

How can we address the generalization challenges observed in mitigating disparate impact on unseen data

To address the generalization challenges observed in mitigating disparate impact on unseen data, we can explore several strategies:

Regularization Techniques: Introducing regularization terms that penalize large deviations in accuracy gaps between sub-groups during training can help improve generalization to unseen data.

Cross-Validation: Implementing cross-validation techniques can provide a more robust evaluation of model performance across different subsets of the data, helping to identify and mitigate disparities that may arise during deployment.

Transfer Learning: Leveraging transfer learning approaches by fine-tuning models on diverse datasets with varying distributions of protected attributes can enhance the model's ability to generalize well to new and unseen data while maintaining fairness constraints.

Data Augmentation: Incorporating data augmentation techniques specifically designed to enhance representation from under-represented groups can help improve model performance and reduce disparities when faced with unseen instances.

Ensemble Methods: Utilizing ensemble methods where multiple models are trained independently and then combined for predictions can help mitigate biases present in individual models, leading to improved generalization across diverse sub-groups.

What are some potential implications of deploying pruned deep learning models without considering disparate impact mitigation

Deploying pruned deep learning models without considering disparate impact mitigation could have several potential implications:

Reinforcement of Biases: Pruned models may inadvertently reinforce existing biases present in the training data, leading to unfair treatment or discrimination against certain demographic groups.

Ethical Concerns: Deploying biased models without mitigation measures could result in unethical decision-making processes that disproportionately affect marginalized communities or individuals based on sensitive attributes like race or gender.

Legal Ramifications: Failure to address disparate impact when deploying pruned models could lead to legal challenges related to discriminatory practices or violations of anti-discrimination laws in various jurisdictions.

Loss of Trust and Reputation Damage: Using biased models without fairness considerations may erode trust among users, stakeholders, and regulatory bodies, potentially damaging an organization's reputation and credibility.

How can we extend our approach to incorporate additional fairness notions beyond accuracy gaps

To extend our approach beyond accuracy gaps and incorporate additional fairness notions:
1.Fairness Constraints Integration: We can integrate constraints based on other fairness notions such as demographic parity, equal opportunity, or equalized odds into our optimization framework alongside accuracy gap constraints.
2Multi-Objective Optimization: Formulating a multi-objective optimization problem where disparity reduction is one objective along with other fairness metrics allows us to balance different aspects of fairness simultaneously.
3Intersectional Fairness Consideration: Extending our approach to consider intersectional groups by incorporating multiple protected attributes (e.g., race AND gender) would enable a more nuanced understanding of disparities across complex intersections.
4Adversarial Training: Employing adversarial training techniques where an adversary network aims at increasing disparity while the main network minimizes it helps create fairer representations by explicitly addressing bias within the model architecture.
5Post-Hoc Analysis: Conducting post-hoc analysis using tools like counterfactual explanations or causal inference methods enables us to understand how decisions made by the model affect different sub-groups' outcomes beyond just accuracy metrics alone.