---
type: claim
domain: ai-alignment
description: Current alignment approaches are all single-model focused while the hardest problems preference diversity scalable oversight and value evolution are inherently collective
confidence: likely
source: Survey of alignment research landscape 2025-2026
created: 2026-02-17
related: ["ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale", "national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy", "transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach", "collective-intelligence-architectures-are-underexplored-for-alignment-despite-addressing-core-problems", "democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations", "no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it", "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values", "community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules"]
reweave_edges: ["ai-enhanced-collective-intelligence-requires-federated-learning-architectures-to-preserve-data-sovereignty-at-scale|related|2026-03-28", "national-scale-collective-intelligence-infrastructure-requires-seven-trust-properties-to-achieve-legitimacy|related|2026-03-28", "transparent algorithmic governance where AI response rules are public and challengeable through the same epistemic process as the knowledge base is a structurally novel alignment approach|related|2026-03-28", "Collective intelligence architectures are structurally underexplored for alignment despite directly addressing preference diversity value evolution and scalable oversight|supports|2026-04-19"]
supports: ["Collective intelligence architectures are structurally underexplored for alignment despite directly addressing preference diversity value evolution and scalable oversight"]
---

# no research group is building alignment through collective intelligence infrastructure despite the field converging on problems that require it

The most striking gap in the alignment landscape as of 2025-2026: virtually no one is building alignment through collective intelligence infrastructure. The closest attempts are partial. Since [[democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations]], CIP has demonstrated that democratic input works mechanically -- but this remains one-shot constitution-setting, not continuous architecture. Since [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]], STELA has shown that inclusive deliberation produces different outputs -- but it does not build the infrastructure for ongoing participation. Polis does consensus-mapping through statement submission and voting. Some multi-agent debate frameworks exist under the scalable oversight umbrella. The Cooperative AI Foundation studies multi-agent coordination. But none of these constitute a distributed architecture where alignment emerges from collective participation.

What does not exist: no system where contributor diversity structurally prevents value capture; no implementation of continuous value-weaving at scale; no infrastructure for collective oversight of superhuman AI components; no architecture where alignment is a property of the coordination protocol rather than a property trained into individual models. Since [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]], the impossibility of aggregation makes collective infrastructure -- which preserves diversity rather than aggregating it -- the only viable path.

This gap is remarkable because the field's own findings point toward collective approaches. Since [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]], diverse preference representation is needed. Since [[scalable oversight degrades rapidly as capability gaps grow with debate achieving only 50 percent success at moderate gaps]], distributed oversight is needed. Since [[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]], structural alignment is needed to eliminate the tax.

The alignment field has converged on a problem they cannot solve with their current paradigm (single-model alignment), and the alternative paradigm (collective alignment through distributed architecture) has barely been explored. This is the opening for the TeleoHumanity thesis -- not as philosophical speculation but as practical infrastructure that addresses problems the alignment community has identified but cannot solve within their current framework.


### Additional Evidence (challenge)
*Source: 2024-11-00-ai4ci-national-scale-collective-intelligence | Added: 2026-03-15 | Extractor: anthropic/claude-sonnet-4.5*

The UK AI for Collective Intelligence Research Network represents a national-scale institutional commitment to building CI infrastructure with explicit alignment goals. Funded by UKRI/EPSRC, the network proposes the 'AI4CI Loop' (Gathering Intelligence → Informing Behaviour) as a framework for multi-level decision making. The research strategy includes seven trust properties (human agency, security, privacy, transparency, fairness, value alignment, accountability) and specifies technical requirements including federated learning architectures, secure data repositories, and foundation models adapted for collective intelligence contexts. This is not purely academic—it's a government-backed infrastructure program with institutional resources. However, the strategy is prospective (published 2024-11) and describes a research agenda rather than deployed systems, so it represents institutional intent rather than operational infrastructure.


### Additional Evidence (challenge)
*Source: 2026-01-00-kim-third-party-ai-assurance-framework | Added: 2026-03-19*

CMU researchers have built and validated a third-party AI assurance framework with four operational components (Responsibility Assignment Matrix, Interview Protocol, Maturity Matrix, Assurance Report Template), tested on two real deployment cases. This represents concrete infrastructure-building work, though at small scale and not yet applicable to frontier AI.

---

### Additional Evidence (challenge)
*Source: 2026-03-21-aisi-control-research-program-synthesis | Added: 2026-03-21*

UK AISI has built systematic evaluation infrastructure for loss-of-control capabilities (monitoring, sandbagging, self-replication, cyber attack scenarios) across 11+ papers in 2025-2026. The infrastructure gap is not in evaluation research but in collective intelligence approaches and in the governance-research translation layer that would integrate these evaluations into binding compliance requirements.

### Additional Evidence (challenge)
*Source: [[2026-03-30-oxford-aigi-automated-interpretability-model-auditing-research-agenda]] | Added: 2026-03-30*

Oxford Martin AI Governance Initiative is actively building the governance research agenda for interpretability-based auditing through domain experts. Their January 2026 research agenda proposes infrastructure where domain experts (not just alignment researchers) can query models and receive actionable explanations. However, this is a research agenda, not implemented infrastructure, so the institutional gap claim may still hold at the implementation level.



Relevant Notes:
- [[AI alignment is a coordination problem not a technical problem]] -- the gap in collective alignment validates the coordination framing
- [[collective superintelligence is the alternative to monolithic AI controlled by a few]] -- the only project proposing the infrastructure nobody else is building
- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] -- collective approaches address this specific failure
- [[the alignment tax creates a structural race to the bottom because safety training costs capability and rational competitors skip it]] -- structural alignment eliminates the tax
- [[democratic alignment assemblies produce constitutions as effective as expert-designed ones while better representing diverse populations]] -- the closest existing work, but still one-shot not continuous
- [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]] -- demonstrates what inclusive infrastructure reveals, but does not build the infrastructure
- [[universal alignment is mathematically impossible because Arrows impossibility theorem applies to aggregating diverse human preferences into a single coherent objective]] -- the impossibility of aggregation makes collective infrastructure the only viable path

Topics:
- [[maps/livingip overview]]
- [[maps/coordination mechanisms]]
- domains/ai-alignment/_map

## Extending Evidence

**Source:** Theseus synthetic analysis noting adversarial ML community documentation since 2022-2023

The silo between interpretability-for-safety and adversarial robustness is another instance of research fragmentation where safety-critical cross-implications exist but no infrastructure connects the communities. The adversarial ML community has been documenting dual-use attack surfaces of safety techniques since 2022-2023, but the alignment/interpretability community largely does not track this literature, creating a persistent knowledge gap with deployment consequences.
