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Evidence record 1242 · automatically gathered

Pareto-Guided Teacher Alignment for Fair Personalized Text Generation

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 s

Record details

Published: 8 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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arXiv · 1 June 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

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arXiv · 31 May 2026

Shared topic: Bias & fairness · Shared topic: Safety & alignment

Improving Visual Representation Alignment Generation with GRPO

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How to cite this record

ethics.ai (8 June 2026), “Pareto-Guided Teacher Alignment for Fair Personalized Text Generation,” evidence record 1242, https://ethics.ai/record/1242 (originally published by arXiv).

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