Enterprise AI Analysis
Promising Topics for US-China Dialogues on AI Risks and Governance
This paper identifies potential common ground for productive dialogue between the United States and China on AI risks and governance. Through a systematic analysis of over 40 primary AI policy and corporate governance documents from both nations, it highlights areas of strong and moderate overlap in risk perception (e.g., limited user transparency, poor reliability, bias, dangerous capabilities, weak cybersecurity) and governance approaches (e.g., AI for pro-safety purposes, stakeholder convening, external auditing, licensing). The research suggests concrete opportunities for bilateral cooperation despite geopolitical tensions, contributing to understanding how international governance frameworks might be harmonized.
Executive Impact & Key Insights
The U.S. and China, leading AI powers, acknowledge the need for effective global AI governance and safety, as evidenced by their participation in initiatives like the Bletchley Declaration and UN resolutions. Despite ongoing geopolitical tensions and strategic competition, this analysis reveals significant common ground. Key areas of convergence include concerns about algorithmic transparency, system reliability, and the importance of multi-stakeholder engagement. Both nations also recognize AI's potential for enhancing safety. This shared understanding provides a basis for productive bilateral dialogues, which could facilitate harmonized international governance frameworks for responsible AI development.
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Documents Analyzed
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Areas of Strong Overlap
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Areas of Moderate Overlap
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Strong Overlap
in User Transparency & Reliability
Both the U.S. NIST Risk Management Framework and Chinese AI safety standards emphasize the importance of limited user transparency, requiring clear disclosure of AI system information and outputs. Similarly, poor reliability is a shared concern, with both nations calling for standardization and specific reliability testing requirements for AI systems.
Moderate Overlap in Robustness, Bias, and Dangerous Capabilities
- Lack of Robustness: Both sides acknowledge this, with NIST defining it as maintaining performance under various circumstances, while Chinese sources integrate it into discussions of generative AI's 'black-box' nature.
- Bias & Discrimination: Western and Chinese documents address this, particularly regarding race, ethnicity, sex, and religion. However, Chinese labs note that their benchmarks might not fully account for non-Chinese contexts.
- Dangerous Capabilities (CBRN & Cyber): Strong overlap exists regarding CBRN risks (chemical, biological, radiological, nuclear), though the U.S. frames it more broadly for AI's role in development vs. content security in China. Concerns about AI's cyber capabilities (e.g., generating malware) are also shared.
Strong Consensus
on AI for Pro-Safety Purposes & Stakeholder Convening
There is strong consensus that AI can be leveraged for safety, such as identifying cyber threats and evaluating models. Both the U.S. (Biden AI EO) and China (Deep Synthesis regulations) advocate for multi-stakeholder engagement, involving interagency councils, private sector, academia, and civil society, to guide AI development.
Technical Solutions and Evaluations
- Technical Solutions (Content Provenance): Significant overlap on the need for content provenance and labeling to ensure information traceability, with the U.S. focusing on 'software bills of materials' and China on implicit identifiers.
- Evaluations (Pre/Post-Deployment): Both nations agree on the necessity of pre- and post-deployment evaluations and monitoring. The U.S. emphasizes guidance on benchmarks for system function and resilience, while China focuses on discovering safety issues during service provision. Standardization of evaluations is also a shared goal.
Enterprise Process Flow
Initial Policy Documents (2021-2023)
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Identification of Shared Risk Perceptions
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Analysis of Convergent Governance Approaches
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Bilateral Dialogue & Standards Development
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Harmonized Responsible AI Implementation
| Area | U.S. Perspective | China Perspective | Potential for Cooperation |
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| Model Evaluation (National Security) |
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| Technical Standards (Commercial Safety) |
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| Industry Coordination (Information Traceability) |
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| Emerging Governance (Compute Thresholds) |
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The Bletchley Declaration: A Precedent for Cooperation
The Bletchley Declaration, signed by both the U.S. and China at the 2023 AI Safety Summit, serves as a crucial precedent for international cooperation. It acknowledged shared concerns about AI's risks to human rights, privacy, fairness, and potential catastrophic harm.
Challenge: The main challenge was aligning diverse national interests and regulatory philosophies to agree on common AI risks.
Solution: The summit facilitated dialogue and a shared recognition of global AI risks, demonstrating that even amidst strategic competition, common ground can be found on critical safety issues, paving the way for future bilateral and multilateral engagements.
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Implementation Roadmap
A phased approach to integrate these insights into your AI strategy for responsible and effective deployment.
Phase 1: Bilateral Risk Assessment Dialogue
Initiate focused discussions between U.S. and Chinese experts to harmonize AI risk taxonomies and prioritize areas of common concern, particularly regarding dual-use capabilities and model safety. (Estimated: 3-6 months)
Phase 2: Joint Technical Standards Working Groups
Establish working groups under existing international standards bodies (e.g., ISO) or new dedicated fora to develop shared technical standards for AI reliability, robustness, and adversarial testing. (Estimated: 6-12 months)
Phase 3: Information Traceability & Watermarking Initiatives
Launch collaborative projects, possibly involving the C2PA, to develop and implement common mechanisms for content provenance, digital watermarking, and prevention of model weight theft. (Estimated: 9-15 months)
Phase 4: Exchange Programs on AI Governance Best Practices
Facilitate expert exchanges and workshops to share best practices on AI governance mechanisms, including compute thresholds, model registration systems, and AI's role in improving safety evaluations. (Estimated: 12-18 months)
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