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 through supervised fine-tuning and direct preference optimization. We evaluate the framework on climate change and vaccination persuasion tasks using a controlled context-rich demographic grid with matched gender and age pairs and a unified five-audit evaluation suite spanning persuasion bias, formality disparity, emotional framing disparity, lexical association disparity, and personalization fidelity. Across both domains and cross-family transfer settings, no single alignment strategy dominates all objectives simultaneously. Instead, methods occupy different regions of a fairness-personalization Pareto frontier: some achieve stronger disparity reductions, while others better preserve personalization or demographic stability. Our results show that fairness mitigation effects are objective-dependent and transfer inconsistently across domains and model families, motivating bounded-regression, multi-audit model selection over single-metric optimization for fairness-sensitive personalized generation.

翻译：个性化说服性文本生成能提升相关性与用户参与度，但基于人口统计特征的条件约束可能在不同群体间引发不平等框架。我们将个性化生成中的公平性缓解问题建模为受约束的多目标对齐问题：在保持个性化保真度的同时减少人口统计差异。我们提出一种Pareto引导的教师对齐框架，该框架融合了基于修正的候选生成、成对感知可行性门控、Pareto式候选选择，以及通过监督微调和直接偏好优化实现的可选偏好优化。我们使用一个包含性别与年龄配对匹配的受控、富上下文人口统计网格，以及涵盖说服偏差、形式性差异、情感框架差异、词汇关联差异和个性化保真度的统一五维度审计评估套件，在气候变化与疫苗接种说服任务上评估该框架。跨领域与跨模型族迁移场景下，单一对齐策略无法同时主导所有目标。相反，不同方法占据公平性-个性化Pareto前沿的不同区域：部分方法实现更强的差异缩减，而另一些更佳地保持个性化或人口统计稳定性。结果表明，公平性缓解效应具有目标依赖性，且在不同领域与模型族间迁移效果不一致，这促使在面向公平性敏感的个性化生成时，采用有界回归、多维度审计的模型选择而非单指标优化。