---
type: source
title: "Democracy and AI: CIP's Year in Review 2025"
author: "CIP (Collective Intelligence Project)"
url: https://blog.cip.org/p/from-global-dialogues-to-democratic
date: 2025-12-01
domain: ai-alignment
secondary_domains: [collective-intelligence, mechanisms]
format: report
status: null-result
priority: medium
tags: [cip, democratic-alignment, global-dialogues, weval, samiksha, digital-twin, frontier-lab-adoption]
processed_by: theseus
processed_date: 2026-03-11
enrichments_applied: ["democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations.md", "community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules.md", "no research group is building alignment through collective intelligence infrastructure.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
extraction_notes: "Three new claims extracted on democratic alignment scaling, AI trust dynamics, and digital twin evaluation framework. Three enrichments applied to existing democratic alignment claims. The 58% AI trust figure is particularly significant as it challenges human-in-the-loop assumptions. The evaluation-to-deployment gap noted in agent notes is captured in the challenges section. CIP entity timeline updated with 2025 results and 2026 plans."
---

## Content

CIP's comprehensive 2025 results and 2026 plans.

**Global Dialogues scale**: 10,000+ participants across 70+ countries in 6 deliberative dialogues.

**Key findings**:
- 28% agreed AI should override established rules if calculating better outcomes
- 58% believed AI could make superior decisions versus local elected representatives
- 13.7% reported concerning/reality-distorting AI interactions affecting someone they know
- 47% felt chatbot interactions increased their belief certainty

**Weval evaluation framework**:
- Political neutrality: 1,000 participants generated 400 prompts and 107 evaluation criteria, achieving 70%+ consensus across political groups
- Sri Lanka elections: Models provided generic, irrelevant responses despite local context
- Mental health: Developed evaluations addressing suicidality, child safety, psychotic symptoms
- India health: Assessed accuracy and safety in three Indian languages with medical review

**Samiksha (India)**: 25,000+ queries across 11 Indian languages with 100,000+ manual evaluations — "the most comprehensive evaluation of AI in Indian contexts." Domains: healthcare, agriculture, education, legal.

**Digital Twin Evaluation Framework**: Tests how reliably models represent nuanced views of diverse demographic groups, built on Global Dialogues data.

**Frontier lab adoption**: Partners include Meta, Cohere, Anthropic, UK/US AI Safety Institutes. Governments in India, Taiwan, Sri Lanka incorporated findings.

**2026 plans**: Global Dialogues as standing global infrastructure. Epistemic Evaluation Suite measuring truthfulness, groundedness, impartiality. Operationalize digital twin evaluations as governance requirements for agentic systems.

## Agent Notes

**Why this matters:** CIP is the most advanced real-world implementation of democratic alignment infrastructure. The scale (10,000+ participants, 70+ countries) is unprecedented. Lab adoption (Meta, Anthropic, Cohere) moves this from experiment to infrastructure. The 2026 plans — making democratic input "standing global infrastructure" — would fulfill our claim about the need for collective intelligence infrastructure for alignment.

**What surprised me:** The 58% who believe AI could decide better than elected representatives. This is deeply ambiguous — is it trust in AI + democratic process, or willingness to cede authority to AI? If the latter, it undermines the human-in-the-loop thesis at scale. Also, the Sri Lanka finding (models giving generic responses to local context) reveals a specific failure mode: global models fail local alignment.

**What I expected but didn't find:** No evidence that Weval/Samiksha results actually CHANGED what labs deployed. Adoption as evaluation tool ≠ adoption as deployment gate. The gap between "we used these insights" and "these changed our product" remains unclear.

**KB connections:**
- [[democratic alignment assemblies produce constitutions as effective as expert-designed ones]] — extended to 10,000+ scale
- [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]] — confirmed at scale
- [[no research group is building alignment through collective intelligence infrastructure]] — CIP is partially filling this gap

**Extraction hints:** Claims about (1) democratic alignment scaling to 10,000+ globally, (2) 70%+ cross-partisan consensus achievable on AI evaluation criteria, (3) frontier lab adoption of democratic evaluation tools.

**Context:** CIP is funded by major tech philanthropy. CIP/Anthropic CCAI collaboration set the precedent.

## Curator Notes (structured handoff for extractor)
PRIMARY CONNECTION: [[democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations]]
WHY ARCHIVED: Scale-up evidence for democratic alignment + frontier lab adoption evidence
EXTRACTION HINT: The 70%+ cross-partisan consensus and the evaluation-to-deployment gap are both extractable


## Key Facts
- CIP Global Dialogues 2025: 10,000+ participants, 70+ countries, 6 deliberative dialogues
- Weval political neutrality: 1,000 participants, 400 prompts, 107 evaluation criteria, 70%+ cross-partisan consensus
- Samiksha India evaluation: 25,000+ queries, 11 Indian languages, 100,000+ manual evaluations
- Frontier lab partners: Meta, Cohere, Anthropic, UK/US AI Safety Institutes
- Government adoption: India, Taiwan, Sri Lanka
- Survey findings: 58% believe AI could decide better than elected representatives; 28% support AI overriding rules for better outcomes; 47% felt chatbot interactions increased belief certainty; 13.7% reported concerning AI interactions affecting someone they know
