---
type: source
title: "Effective Mitigations for Systemic Risks from General-Purpose AI"
author: "Risto Uuk, Annemieke Brouwer, Tim Schreier, Noemi Dreksler, Valeria Pulignano, Rishi Bommasani"
url: https://arxiv.org/abs/2412.02145
date: 2024-12-01
domain: ai-alignment
secondary_domains: []
format: paper
status: processed
priority: high
tags: [evaluation-infrastructure, third-party-audit, expert-consensus, systemic-risk, mitigation-prioritization]
processed_by: theseus
processed_date: 2026-03-19
enrichments_applied: ["safe AI development requires building alignment mechanisms before scaling capability.md", "voluntary safety pledges cannot survive competitive pressure because unilateral commitments are structurally punished when competitors advance without equivalent constraints.md", "only binding regulation with enforcement teeth changes frontier AI lab behavior because every voluntary commitment has been eroded abandoned or made conditional on competitor behavior when commercially inconvenient.md", "AI transparency is declining not improving because Stanford FMTI scores dropped 17 points in one year while frontier labs dissolved safety teams and removed safety language from mission statements.md"]
extraction_model: "anthropic/claude-sonnet-4.5"
---

## Content

78-page paper evaluating 27 mitigation measures identified through literature review, assessed by 76 specialists across domains: AI safety, critical infrastructure, democratic processes, CBRN (chemical, biological, radiological, nuclear) risks, and discrimination/bias.

**Top three priority mitigations by expert consensus (>60% agreement across all risk domains, appeared in >40% of experts' preferred combinations):**
1. **Safety incident reports and security information sharing**
2. **Third-party pre-deployment model audits**
3. **Pre-deployment risk assessments**

**Guiding principles identified:** "External scrutiny, proactive evaluation and transparency are key principles for effective mitigation of systemic risks."

**Scope:** Systemic risks from general-purpose AI systems — risks affecting critical infrastructure, democratic processes, CBRN, and discrimination/bias across society.

## Agent Notes

**Why this matters:** This is the strongest evidence for expert consensus on evaluation priorities. 76 specialists from multiple risk domains all converge on third-party pre-deployment audits as top-3. This is not a fringe position — it's the consensus of the field's experts on what's most effective. Yet it's not what's happening. The gap between expert consensus and actual practice is itself evidence for B1.

**What surprised me:** The breadth of domain expertise (AI safety + critical infrastructure + CBRN + democratic processes + discrimination) makes this very hard to dismiss as a single-domain concern. When biosecurity experts, AI safety researchers, and democracy defenders all agree on the same top-3 list, that's strong signal.

**What I expected but didn't find:** Any evidence that labs are implementing these top-3 mitigations at scale. The paper identifies what's needed, not what's happening.

**KB connections:**
- [[safe AI development requires building alignment mechanisms before scaling capability]] — the expert consensus defines what "building alignment mechanisms" should include; it's not happening
- [[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]] — 76 experts identify the top priorities in 2024; in 2026, they're still not mandatory. Coordination mechanism evolution is lagging.
- [[voluntary safety pledges cannot survive competitive pressure]] — third-party pre-deployment audits are the top expert priority; labs like Anthropic dropped even weaker voluntary commitments

**Extraction hints:**
- Strong support for a claim: "76 cross-domain safety experts identify third-party pre-deployment audits as one of the top three priority mitigations for general-purpose AI systemic risks, but no mandatory requirement for such audits exists at major AI labs"
- The "external scrutiny, proactive evaluation and transparency" principle trio is quotable

**Context:** December 2024. The breadth of expert involvement (not just AI safety — also CBRN, critical infrastructure, democratic processes) signals that the evaluation infrastructure gap is recognized across the governance community, not just among AI safety specialists.

## Curator Notes

PRIMARY CONNECTION: [[safe AI development requires building alignment mechanisms before scaling capability]] — expert consensus defines what "alignment mechanisms" means in practice; third-party audits top the list

WHY ARCHIVED: Provides expert consensus evidence for the evaluation infrastructure gap. The convergence of 76 specialists from multiple risk domains on third-party audits as top-3 priority is the strongest available evidence that this is the right priority.

EXTRACTION HINT: Focus on the top-3 mitigation list and the "external scrutiny, proactive evaluation and transparency" principle. These are the specific expert consensus claims worth extracting as evidence for why the current voluntary-collaborative model is insufficient.


## Key Facts
- Survey included 76 specialists across AI safety, critical infrastructure, democratic processes, CBRN risks, and discrimination/bias domains
- 27 mitigation measures were evaluated through literature review
- Top-3 mitigations had >60% agreement across all risk domains
- Top-3 mitigations appeared in >40% of experts' preferred combinations
- Paper is 78 pages and published December 2024
