extract: 2025-11-00-operationalizing-pluralistic-values-llm-alignment

Pentagon-Agent: Ganymede <F99EBFA6-547B-4096-BEEA-1D59C3E4028A>

domains/ai-alignment/community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md

@@ -19,6 +19,12 @@ Since [[democratic alignment assemblies produce constitutions as effective as ex
 
 Since [[collective intelligence requires diversity as a structural precondition not a moral preference]], community-centred norm elicitation is a concrete mechanism for ensuring the structural diversity that collective alignment requires. Without it, alignment defaults to the values of whichever demographic builds the systems.
 
+
+### Additional Evidence (confirm)
+*Source: [[2025-11-00-operationalizing-pluralistic-values-llm-alignment]] | Added: 2026-03-15*
+
+Empirical study with 27,375 ratings from 1,095 participants shows that demographic composition of training data produces 3-5 percentage point differences in model behavior across emotional awareness and toxicity dimensions. This quantifies the magnitude of difference between community-sourced and developer-specified alignment targets.
+
 ---
 
 Relevant Notes:

domains/ai-alignment/single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.md

@@ -27,6 +27,12 @@ Chakraborty, Qiu, Yuan, Koppel, Manocha, Huang, Bedi, Wang. "MaxMin-RLHF: Alignm
 - GPT-2 experiment: single RLHF achieved positive sentiment but ignored conciseness
 - Tulu2-7B experiment: minority group accuracy dropped from 70.4% to 42% at 10:1 ratio
 
+
+### Additional Evidence (confirm)
+*Source: [[2025-11-00-operationalizing-pluralistic-values-llm-alignment]] | Added: 2026-03-15*
+
+Study demonstrates that models trained on different demographic populations show measurable behavioral divergence (3-5 percentage points), providing empirical evidence that single-reward functions trained on one population systematically misalign with others.
+
 ---
 
 Relevant Notes:

domains/ai-alignment/some disagreements are permanently irreducible because they stem from genuine value differences not information gaps and systems must map rather than eliminate them.md

@@ -21,6 +21,12 @@ The correct response is to map the disagreement rather than eliminate it. Identi
 
 [[Collective intelligence within a purpose-driven community faces a structural tension because shared worldview correlates errors while shared purpose enables coordination]]. Persistent irreducible disagreement is actually a safeguard here -- it prevents the correlated error problem by maintaining genuine diversity of perspective within a coordinated community. The independence-coherence tradeoff is managed not by eliminating disagreement but by channeling it productively.
 
+
+### Additional Evidence (confirm)
+*Source: [[2025-11-00-operationalizing-pluralistic-values-llm-alignment]] | Added: 2026-03-15*
+
+Systematic variation of demographic composition in alignment training produced persistent behavioral differences across Liberal/Conservative, White/Black, and Female/Male populations, suggesting these reflect genuine value differences rather than information asymmetries that could be resolved.
+
 ---
 
 Relevant Notes:

inbox/archive/.extraction-debug/2025-11-00-operationalizing-pluralistic-values-llm-alignment.json

@@ -0,0 +1,24 @@
+{
+  "rejected_claims": [
+    {
+      "filename": "demographic-composition-of-alignment-training-data-produces-measurable-behavioral-differences-in-model-outputs.md",
+      "issues": [
+        "missing_attribution_extractor"
+      ]
+    }
+  ],
+  "validation_stats": {
+    "total": 1,
+    "kept": 0,
+    "fixed": 1,
+    "rejected": 1,
+    "fixes_applied": [
+      "demographic-composition-of-alignment-training-data-produces-measurable-behavioral-differences-in-model-outputs.md:set_created:2026-03-15"
+    ],
+    "rejections": [
+      "demographic-composition-of-alignment-training-data-produces-measurable-behavioral-differences-in-model-outputs.md:missing_attribution_extractor"
+    ]
+  },
+  "model": "anthropic/claude-sonnet-4.5",
+  "date": "2026-03-15"
+}

inbox/archive/2025-11-00-operationalizing-pluralistic-values-llm-alignment.md

@@ -7,9 +7,13 @@ date: 2025-11-01
 domain: ai-alignment
 secondary_domains: []
 format: paper
-status: unprocessed
+status: enrichment
 priority: high
 tags: [pluralistic-alignment, demographic-composition, empirical, safety-inclusivity, real-human-feedback]
+processed_by: theseus
+processed_date: 2026-03-15
+enrichments_applied: ["community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md", "single-reward-rlhf-cannot-align-diverse-preferences-because-alignment-gap-grows-proportional-to-minority-distinctiveness.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"]
+extraction_model: "anthropic/claude-sonnet-4.5"
 ---
 
 ## Content
@@ -37,3 +41,11 @@ Demonstrates that "whose feedback" matters as much as "how much feedback" for al
 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 27,375 ratings from 1,095 participants
+- Models fine-tuned on Liberal feedback showed 5.0 percentage point improvement over Conservative baseline
+- Models fine-tuned on White feedback showed 4.7 percentage point improvement over Black baseline
+- Models fine-tuned on Female feedback showed 3.4 percentage point improvement over Male baseline
+- Effects measured across emotional awareness and toxicity dimensions