arXiv:2606.10126v1 Announce Type: new Abstract: Personalized persuasive text generation can improve relevance and engagement, but demographic conditioning may also introduce unequal framing across groups. We study fairness mitigation in personalized generation as a constrained multi-objective alignment problem: reduce demographic disparities while preserving personalization fidelity. We propose a Pareto-guided teacher alignment framework that combines revision-based candidate generation, pair-aware feasibility gating, Pareto-style candidate selection, and optional preference optimization throu
The rapid advancement and deployment of personalized text generation models necessitate robust fairness and bias mitigation strategies to ensure ethical and equitable application.
Ensuring fairness in personalized AI systems is critical for maintaining public trust, preventing disparate impacts, and complying with future regulatory frameworks surrounding AI ethics.
This research introduces a novel framework for balancing personalization with fairness in text generation, offering a method to systematically mitigate demographic bias.
- · AI developers
- · Ethical AI frameworks
- · Consumers of personalized content
- · Platforms with unmitigated biased AI
- · Generative AI models lacking fairness controls
Personalized text generation can be deployed with reduced demographic disparities.
Improved trust in AI systems due to fairer outcomes will accelerate wider adoption.
New industry standards for 'fair-by-design' AI will emerge, requiring these kinds of mitigation techniques.
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