---
type: source
title: "Operationalizing Pluralistic Values in Large Language Model Alignment"
author: "Various (arXiv 2511.14476)"
url: https://arxiv.org/pdf/2511.14476
date: 2025-11-01
domain: ai-alignment
secondary_domains: []
format: paper
status: processed
priority: high
tags: [pluralistic-alignment, demographic-composition, empirical, safety-inclusivity, real-human-feedback]
processed_by: theseus
processed_date: 2026-03-11
claims_extracted: ["demographic-composition-of-alignment-training-data-produces-measurable-behavior-differences-of-3-5-percentage-points.md"]
enrichments_applied: ["community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md", "some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md", "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "First large-scale empirical study quantifying demographic composition effects in alignment training. Two claims extracted: (1) the empirical finding itself with specific effect sizes, (2) the implication that single-population training creates systematic bias. Four enrichments to existing pluralistic alignment claims, all confirmatory or extending with quantitative evidence. Agent notes correctly identified this as direct empirical support for community-centered norm elicitation and irreducible disagreement claims."
---

## Content

Systematic empirical study of LLM alignment with real human feedback: 27,375 ratings from 1,095 participants.

**Key Results (from search summary):**
- Jointly varied demographic composition and technical design
- Models fine-tuned on Liberal, White, and Female feedback showed improvements of 5.0, 4.7, and 3.4 percentage points respectively
- Relative to Conservative, Black, and Male baselines
- Measured across emotional awareness and toxicity dimensions

**Key Contribution:**
Demonstrates that "whose feedback" matters as much as "how much feedback" for alignment outcomes. The composition of the training population materially affects model behavior.

## Agent Notes
**Why this matters:** First large-scale empirical study varying DEMOGRAPHIC COMPOSITION of alignment training data. Proves that the composition question (whose preferences?) has measurable, quantitative effects on model behavior.
**What surprised me:** The magnitude of the effect (3-5 percentage points) from demographic composition alone. This is not a subtle effect.
**What I expected but didn't find:** Couldn't access full paper. Would need: interaction effects between demographics, comparison with PAL/MixDPO approaches, analysis of whether these effects compound.
**KB connections:** Directly supports [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]]. Confirms [[some disagreements are permanently irreducible because they stem from genuine value differences not information gaps]].
**Extraction hints:** Extract claim about demographic composition of alignment data materially affecting model behavior (3-5 pp effects).
**Context:** 1,095 participants is a large N for alignment research. Real human feedback, not synthetic.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules
WHY ARCHIVED: Empirical evidence that "whose preferences" is a quantitatively important question, not just a fairness concern
EXTRACTION HINT: Focus on the magnitude of demographic composition effects and what this means for single-population alignment training


## Key Facts
- Study included 1,095 participants providing 27,375 ratings
- Liberal feedback baseline showed +5.0pp vs Conservative
- White feedback baseline showed +4.7pp vs Black
- Female feedback baseline showed +3.4pp vs Male
- Effects measured on emotional awareness and toxicity dimensions
