arXiv:2505.22703v2 Announce Type: replace Abstract: Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds, etc.). In this work, we study such constrained minimization problems under differential privacy (DP). Standard DP optimization techniques like DP-SGD rely on objectives that decompose over individual examples, enabling per-example gradient clipping and noise addition. Rate constraints, however, depend on aggregate statistics across groups, creating inter-sample dep
The increasing focus on trustworthy and ethical AI, particularly fairness and privacy, makes advancements in differentially private optimization for constrained learning critically timely.
This research addresses a fundamental challenge in deploying fair and private AI systems, potentially enabling broader adoption of AI in sensitive domains without compromising ethical principles.
The ability to enforce complex rate constraints like fair learning metrics under differential privacy changes the landscape for building robust and ethically aligned AI models, moving beyond simple objective functions.
- · AI ethicists
- · Organizations handling sensitive data
- · Regulators pushing for explainable and fair AI
- · Data privacy solution providers
- · Adversarial actors seeking to exploit data
- · Developers unable to implement private and fair AI
More private and fair AI models become technically feasible for deployment in regulated sectors.
Increased consumer trust in AI systems due to demonstrable privacy and fairness guarantees could accelerate AI adoption.
New regulatory frameworks may emerge, mandating such private and fair learning techniques for AI applications in critical areas.
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Read at arXiv cs.LG