Mitigating Disparate Impact of Differentially Private Learning through Bounded Adaptive Clipping
arXiv:2506.01396v2 Announce Type: replace Abstract: Differential privacy (DP) has become an essential framework for privacy-preserving machine learning. Existing DP learning methods, however, often have disparate impacts on model predictions, e.g., for minority groups. Gradient clipping, which is often used in DP learning, can suppress larger gradients from challenging samples. We show that this problem is amplified by adaptive clipping, which will often shrink the clipping bound to tiny values to match a well-fitting majority, while significantly reducing the accuracy for others. We propose b
The increasing deployment of privacy-preserving machine learning highlights the need to address fairness and mitigate unintended biases that arise during the privacy-preserving process.
This research addresses a critical challenge in AI ethics and deployment, ensuring that privacy-preserving techniques do not inadvertently create or exacerbate disparities, which is crucial for ethical and equitable AI systems.
The proposed 'bounded adaptive clipping' method offers a more equitable approach to differential privacy, potentially leading to fairer outcomes for minority groups in privacy-preserved AI models.
- · AI ethics researchers
- · Underrepresented user groups
- · Companies deploying privacy-preserving AI
- · Machine learning models with unmitigated disparate impact
- · Adaptive clipping as a standalone technique without fairness considerations
Improved fairness and reduced discrimination in AI systems that use differential privacy.
Increased trust and broader adoption of privacy-preserving AI across sensitive applications.
New regulations and industry standards that mandate fairness-aware differential privacy mechanisms.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG