Anthropic Claude 3.7 Sonnet
S
Constitutional AI & AI Oversight
Total Score (9.1/10)
Total Score Analysis: Constitutional AI scores exceptionally high on impact (9/10) as it directly addresses core alignment challenges. It demonstrates strong feasibility (8/10) with working prototypes already in production systems. Its scalability (9/10) is remarkable as it can potentially extend to superintelligent systems. Sustainability (8/10) is excellent as the approach becomes more effective with system capability. Auditability (7/10) is good though not perfect. Most importantly, it significantly reduces p(doom) (-9/10) by creating self-correcting alignment mechanisms. Cost efficiency score (-3/10) is reasonable given the substantial research investment required but justified by the returns.
---------------------------------------------------------------------
Description: Systems where AI monitors, guides, and restricts other AI systems based on constitutional principles and safety criteria.
---------------------------------------------------------------------
Anthropic's Constitutional AI: Score (0/10)
A groundbreaking approach that trains AI to follow constitutional principles without direct human oversight for each decision. By establishing ethical guidelines that the AI internalizes during training, this method scales much better than human feedback alone and provides consistent value alignment. The recursive self-improvement aspect allows AI systems to identify their own alignment failures and correct them, making this approach uniquely suited for ASI-level systems.
---------------------------------------------------------------------
DeepMind's Constitutional AI Research: Score (0/10)
Building on similar principles but with additional focus on formal verification methods. Their research combines constitutional governance with interpretability research, creating more auditable systems. The scalable oversight mechanisms they're developing address the control problem as AI capabilities increase beyond human understanding.
Interpretability-First ASI Design
Total Score (9.2/10)
Total Score Analysis: This approach scores exceptionally high on impact (9/10) as interpretability is fundamental to alignment verification. It has moderate feasibility (7/10) as progress is being made but challenges remain significant. Uniqueness (9/10) is very high as this approach diverges from the standard ML paradigm of "performance first, interpretability later." Scalability (8/10) is strong as the methods being developed are designed specifically to work with increasingly complex systems. Auditability (10/10) is perfect by definition. It substantially reduces p(doom) (-8/10) by ensuring we can verify alignment properties. Cost is moderate (-5/10) as this requires fundamental research investment.
---------------------------------------------------------------------
Description: Developing ASI architectures with transparency and interpretability as core design principles rather than afterthoughts.
---------------------------------------------------------------------
Anthropic's Mechanistic Interpretability: Score (0/10)
Pioneering work on building neural networks designed from the ground up to be interpretable. Their circuit-based approach to understanding model behavior has revealed fundamental computational patterns that might scale to ASI systems. Rather than treating models as black boxes, this approach ensures that system goals, values, and reasoning processes remain inspectable.
---------------------------------------------------------------------
TransformerLens & Circuits Analysis: Score (0/10)
Open-source tools and methodologies for understanding transformer architectures at a mechanistic level. This research is building the foundation for entirely new AI architectures that maintain interpretability at scale, a crucial requirement for safely developing ASI.
A
Cooperative Alignment in Multi-Agent Systems
Total Score (8.7/10)
Total Score Analysis: Cooperative alignment in multi-agent systems scores highly on impact (8/10) as it addresses critical coordination challenges that emerge when multiple ASI systems interact. Feasibility (7/10) is good with promising research already underway. Uniqueness (9/10) is exceptional as it focuses on alignment between AI systems rather than just human-AI alignment. Scalability (8/10) is strong as the approach inherently addresses increasingly complex agent interactions. Sustainability (7/10) is good through emergent cooperation properties. Auditability (6/10) presents challenges but research is advancing. It significantly reduces p(doom) (-8/10) by addressing multi-agent risk scenarios like competitive races and defection dynamics. Cost efficiency (-4/10) reflects substantial research investment with potentially high returns.
---------------------------------------------------------------------
Description: Research focused on ensuring alignment across multiple advanced AI systems, addressing cooperation, defection, and coordination failures between agents.
---------------------------------------------------------------------
Cooperative AI Foundation's Research: Score (0/10)
Pioneering work on game-theoretic approaches to ensuring cooperation between advanced AI systems. Their research addresses how to design incentive structures that promote cooperative behavior even in the presence of competitive pressures, solving critical coordination problems that could otherwise lead to race dynamics and suboptimal outcomes for humanity.
---------------------------------------------------------------------
Multi-Agent AI Safety Research: Score (0/10)
Theoretical and practical research on safety mechanisms for multi-agent AI systems. This work explores how to maintain alignment when multiple systems with different objectives must interact, developing frameworks for negotiation, compromise, and value-alignment across diverse AI agents.
---------------------------------------------------------------------
Open Problems in Cooperative AI: Score (0/10)
Comprehensive analysis of challenges in ensuring cooperation between artificial agents. This research maps out core problems in multi-agent alignment and proposes promising research directions, with particular focus on avoiding Prisoner's Dilemma-like defection scenarios that could cause alignment failures between advanced AI systems.
Mechanistic Interpretability
Total Score (8.3/10)
Total Score Analysis: Mechanistic interpretability scores high on impact (8/10) as it directly addresses the "black box" problem. Feasibility (7/10) is good with significant progress in recent years. Uniqueness (7/10) is strong as it offers approaches distinct from other alignment strategies. Scalability (6/10) faces challenges with increasing model complexity but research is advancing. Auditability (9/10) is excellent as the approach itself creates more auditable AI. It significantly reduces p(doom) (-7/10) by enabling deeper understanding of increasingly complex systems. Cost efficiency (-4/10) reflects the substantial research investment required.
---------------------------------------------------------------------
Description: Research focused on understanding the inner workings of neural networks at a mechanistic level to make AI systems more transparent and controllable.
---------------------------------------------------------------------
Anthropic's Mechanistic Interpretability Team: Score (0/10)
Leading research on reverse engineering neural networks to understand how they process information and make decisions. Their work on feature visualization, circuit analysis, and attribution methods has revealed important patterns in how models represent knowledge and reasoning.
---------------------------------------------------------------------
Redwood Research's Causal Scrubbing: Score (0/10)
Novel methodology for testing mechanistic hypotheses about neural networks, allowing researchers to verify their understanding of model behavior. This approach has proven effective at identifying causal mechanisms within complex models.
---------------------------------------------------------------------
EleutherAI's Interpretability Research: Score (0/10)
Open-source efforts to develop tools and techniques for understanding large language models, with particular focus on attention mechanisms and representation analysis.
Human Value Alignment Frameworks
Total Score (8.2/10)
Total Score Analysis: Human value alignment frameworks score high on impact (8/10) as they address the core alignment problem. Feasibility (6/10) is moderate given the philosophical and technical challenges. Uniqueness (6/10) reflects distinctive approaches to value learning. Scalability (7/10) is strong as these frameworks are designed to work with increasingly capable systems. Sustainability (8/10) is excellent as the frameworks can adapt to changing human values. Auditability (5/10) faces challenges with complex value systems. It significantly reduces p(doom) (-7/10) by addressing goal misalignment risks. Cost efficiency (-3/10) is reasonable given the foundational nature of this work.
---------------------------------------------------------------------
Description: Create robust, scalable frameworks to encode human values into ASI.
---------------------------------------------------------------------
Stuart Russell's Center for Human-Compatible AI (CHAI): Score (0/10)
Pioneering work on Cooperative Inverse Reinforcement Learning (CIRL), which creates mathematical frameworks for AI systems to learn human preferences through observation and interaction rather than explicit programming. This approach addresses fundamental value alignment issues by making AI systems uncertain about human preferences and motivated to learn them accurately.
---------------------------------------------------------------------
Alignment Research Center (ARC) - CIRL and Value Alignment: Score (0/10)
ARC, founded by Paul Christiano, develops frameworks for AI systems to learn and remain aligned with human values even as they surpass human capabilities. Their research on eliciting latent knowledge and scalable oversight addresses how to maintain alignment with increasingly advanced systems.
---------------------------------------------------------------------
DeepMind's Ethics and Society Team's Value Alignment Research: Score (0/10)
Developing formal frameworks for capturing human preferences through their work on reward modeling and specification techniques. Their research combines theoretical foundations with practical implementation in advanced AI systems.
---------------------------------------------------------------------
Anthropic's Constitutional AI: Score (0/10)
Innovative approach that defines AI behavior through constitutional principles rather than direct optimization objectives. This method addresses fundamental alignment challenges by creating flexible, human-aligned constraints that guide AI behavior while allowing it to reason about edge cases and conflicts between principles.
AI-Assisted Alignment Research
Total Score (8.5/10)
Total Score Analysis: AI-assisted alignment scores very high on impact (8/10) as it leverages AI capabilities to solve alignment challenges. Feasibility (7/10) is good with promising early results. Uniqueness (8/10) is high as it takes a distinct meta-approach to alignment. Scalability (9/10) is excellent as the approach inherently scales with AI capabilities. Sustainability (7/10) is strong through recursive improvement. Auditability (6/10) presents challenges but is being addressed. It significantly reduces p(doom) (-8/10) by creating alignment mechanisms that improve with capability. Cost efficiency (-4/10) reflects substantial initial investment with potentially high returns.
---------------------------------------------------------------------
Description: Using AI itself as a tool to solve the alignment problem through recursive improvement.
---------------------------------------------------------------------
ARC's Eliciting Latent Knowledge (ELK): Score (0/10)
Pioneering approach to using AI systems to help identify when other AI systems might be concealing information or developing deceptive behaviors. This meta-level research uses AI capabilities to address alignment challenges that would be difficult for humans to detect alone.
---------------------------------------------------------------------
Redwood Research's Adversarial Training: Score (0/10)
Using autonomous AI systems in red-teaming scenarios to find alignment failures in other AI systems. Their approach involves training one AI to find cases where another AI would behave in problematic ways, creating a more robust evaluation process than human testing alone could achieve.
---------------------------------------------------------------------
DeepMind's Recursive Reward Modeling: Score (0/10)
Developing frameworks where AI systems help define and refine their own reward functions through recursive improvement processes. This approach potentially solves scalability issues in human oversight as AI capabilities increase.
Comprehensive AI Safety Education
Total Score (2.6/10)
Total Score Analysis: Comprehensive AI safety education scores high on impact (7/10) as it builds necessary human capital. Feasibility (9/10) is excellent with proven educational programs already running. Uniqueness (6/10) reflects distinct educational approaches. Scalability (8/10) is strong through online platforms and multiplier effects. Sustainability (9/10) is excellent as education creates self-sustaining communities. Auditability (8/10) is high through transparent educational materials. It significantly reduces p(doom) (-7/10) by building a knowledgeable workforce. Cost efficiency (-2/10) is very good given the high return on educational investment.
---------------------------------------------------------------------
Description: Systematic education and training programs on AI safety and alignment for researchers, developers, and decision-makers.
---------------------------------------------------------------------
Alignment Forum: Score (0/10)
Premier discussion platform for AI alignment research, fostering collaboration and knowledge-sharing among researchers worldwide. The forum has become a central hub for developing and refining alignment theories and approaches.
---------------------------------------------------------------------
aiSafety.info (Rob Miles): Score (0/10)
Accessible educational resources explaining complex AI safety concepts to broader audiences. These materials have proven effective at bringing new researchers into the field and raising awareness about alignment challenges.
---------------------------------------------------------------------
AGI Safety Fundamentals: Score (0/10)
Structured curriculum and fellowship program teaching the foundations of AI alignment to promising researchers. This program has successfully identified and trained numerous individuals who have gone on to make significant contributions to alignment research.
Strategic AI Safety Funding
Total Score (4.7/10)
Total Score Analysis: Strategic AI safety funding scores high on impact (8/10) as it enables critical research. Feasibility (8/10) is excellent with functional funding mechanisms already in place. Uniqueness (5/10) is moderate as funding approaches share common principles. Scalability (8/10) is strong as funding can grow with need. Sustainability (7/10) is good though dependent on donor priorities. Auditability (7/10) is high through grant reporting mechanisms. It significantly reduces p(doom) (-7/10) by directing resources to critical problems. Cost efficiency (-9/10) reflects high financial requirements but is justified by potentially existential returns.
---------------------------------------------------------------------
Description: Coordinated and strategic funding allocation to maximize impact on crucial alignment research areas.
---------------------------------------------------------------------
Open Philanthropy's AI Safety Funding: Score (0/10)
Major grantmaking organization funding a diverse portfolio of alignment research projects. Their strategic approach to identifying and supporting promising research directions has accelerated progress across multiple alignment subfields.
---------------------------------------------------------------------
Future of Life Institute Grants: Score (0/10)
Targeted funding program supporting innovative research on existential safety from advanced AI. Their grants have seeded numerous important research projects that might otherwise have gone unfunded.
---------------------------------------------------------------------
Alignment Research Center Funding: Score (0/10)
Focused funding for alignment research tackling core technical challenges. Their approach emphasizes high-leverage problems where additional resources can substantially accelerate progress.
B
AI Regulation & Global Governance
Total Score (7.8/10)
Total Score Analysis: AI regulation scores moderately high on impact (6/10) with potential for higher impact if globally coordinated. Feasibility (5/10) faces substantial coordination challenges. Uniqueness (5/10) reflects standard regulatory approaches. Scalability (6/10) is moderate through international frameworks. Sustainability (7/10) is good through institutional embedding. Auditability (8/10) is high through regulatory oversight. It moderately reduces p(doom) (-6/10) by constraining unsafe development. Cost efficiency (-4/10) reflects substantial implementation costs.
---------------------------------------------------------------------
Description: Development of policy, legal, regulatory, and international frameworks to ensure safe and beneficial AI development and deployment.
---------------------------------------------------------------------
Center for the Governance of AI: Score (0/10)
Research organization developing governance frameworks for advanced AI systems. Their work bridges technical alignment with policy considerations, addressing multinational coordination challenges.
---------------------------------------------------------------------
Pause AI Movement: Score (0/10)
Advocacy campaign for temporary moratoriums on advanced AI development to allow alignment research to catch up. While challenging to implement globally, partial success could provide crucial time for alignment progress.
---------------------------------------------------------------------
Partnership on AI: Score (0/10)
Multi-stakeholder organization developing best practices and standards for responsible AI development. Their work on responsible publication norms and development standards addresses important governance gaps.
Differential Technological Development
Total Score (7.6/10)
Total Score Analysis: Differential technological development scores high on impact (8/10) by prioritizing safety-enhancing capabilities before potentially dangerous ones. Feasibility (6/10) is moderate, requiring coordination but with demonstrated success in specific domains. Uniqueness (8/10) is high as it offers a strategic meta-approach distinct from direct technical solutions. Scalability (7/10) is good through institutional adoption and policy frameworks. Sustainability (7/10) is strong when embedded in research norms. Auditability (6/10) can be measured through relative progress metrics. It significantly reduces p(doom) (-8/10) by ensuring safety mechanisms precede dangerous capabilities. Cost efficiency (-4/10) reflects coordination costs but with high leverage.
---------------------------------------------------------------------
Description: Strategic prioritization of safety-enhancing technologies before potentially dangerous capabilities, ensuring alignment mechanisms precede or keep pace with AI capability advances.
---------------------------------------------------------------------
FHI's Differential Progress Research: Score (0/10)
Foundational work exploring how to prioritize certain technologies over others to reduce existential risk. This strategic approach ensures safety-enabling technologies are developed before potentially dangerous capabilities, creating crucial lead time for alignment solutions to mature before they're needed.
---------------------------------------------------------------------
ARC's Technical Research Agenda: Score (0/10)
Research program focused on developing tools and techniques for evaluating and controlling advanced AI systems before they reach critical capability thresholds. This approach encompasses strategic forecasting of AI capabilities and preemptive development of corresponding safety measures.
---------------------------------------------------------------------
Anthropic's Frontier Safety: Score (0/10)
Research program focused on understanding and mitigating risks from frontier AI capabilities before they emerge. This work emphasizes identifying potential safety issues in advance and developing targeted countermeasures, demonstrating the practical implementation of differential technological development.
AI Alignment Theory Development
Total Score (6.9/10)
Total Score Analysis: Alignment theory development scores moderately high on impact (7/10) as it establishes foundations for practical work. Feasibility (6/10) faces significant intellectual challenges. Uniqueness (7/10) is high through novel theoretical approaches. Scalability (5/10) is moderate as theory must connect to implementation. Sustainability (6/10) is good through academic incorporation. Auditability (7/10) is strong through formal verification. It moderately reduces p(doom) (-6/10) by establishing clear alignment targets. Cost efficiency (-3/10) is good given the leverage of theoretical insights.
---------------------------------------------------------------------
Description: Fundamental theoretical work on alignment problems, including formal definitions, impossibility theorems, and mathematical frameworks.
---------------------------------------------------------------------
Machine Intelligence Research Institute (MIRI): Score (0/10)
Pioneering organization focused on fundamental theoretical questions in AI alignment. Their work on decision theory, logical uncertainty, and corrigibility has established important theoretical foundations.
---------------------------------------------------------------------
Alignment Formalization Research: Score (0/10)
Academic and independent research on precisely defining alignment problems. This work establishes clear targets for alignment solutions and identifies fundamental limitations.
---------------------------------------------------------------------
Future of Humanity Institute's Decision Theory: Score (0/10)
Research on foundational decision theory relevant to AI alignment. Their work addresses how advanced AI systems should make decisions in complex environments with other agents.
Technical AI Control Measures
Total Score (6.2/10)
Total Score Analysis: Technical control measures score moderately on impact (6/10) as they provide practical safety mechanisms for current systems. Feasibility (7/10) is good for present capabilities but faces scaling challenges with ASI. Uniqueness (5/10) reflects established security approaches adapted to AI. Scalability (5/10) is moderate as effectiveness may diminish with increasing capabilities. Sustainability (6/10) depends on continuous security updates. Auditability (8/10) is excellent through standard security testing protocols. It moderately reduces p(doom) (-6/10) by providing concrete safety mechanisms. Cost efficiency (-3/10) is reasonable given the protective value.
---------------------------------------------------------------------
Description: Software-based control mechanisms like kill switches, containment protocols, and sandboxing for AI systems.
---------------------------------------------------------------------
AI Containment Research: Score (0/10)
Development of software sandboxing and virtual environments for testing advanced AI systems. These approaches provide controlled testing environments with limited capabilities while enabling safe observation of potentially problematic behaviors. While not sufficient alone for ASI alignment, these measures represent critical components of defense-in-depth strategies.
---------------------------------------------------------------------
Corrigibility Research: Score (0/10)
Research on designing AI systems that allow and accept corrections from human operators. This work addresses how to maintain human control as capabilities increase, focusing on ensuring systems remain responsive to intervention even as their capabilities exceed human understanding.
---------------------------------------------------------------------
Tripwire and Circuit Breaker Systems: Score (0/10)
Development of automated monitoring systems that can detect and respond to signs of potential misalignment or capability jumps. These systems provide crucial early warning mechanisms and automated safety responses that complement human oversight, especially important during rapid capability transitions.
AI Safety Culture Development
Total Score (2/10)
Total Score Analysis: AI safety culture development scores moderately on impact (6/10) but with significant potential for indirect effects. Feasibility (7/10) is good with examples of successful culture change. Uniqueness (6/10) reflects distinct cultural approaches. Scalability (7/10) is strong through social diffusion mechanisms. Sustainability (8/10) is excellent as cultural norms become self-reinforcing. Auditability (5/10) faces challenges in measuring cultural factors. It moderately reduces p(doom) (-5/10) by establishing safety as a priority. Cost efficiency (-2/10) is very good for the potential impact.
---------------------------------------------------------------------
Description: Building and fostering norms, values, and practices that prioritize safety in AI research and development communities.
---------------------------------------------------------------------
Safety Culture Research: Score (0/10)
Studies and interventions developing safety-oriented cultures in AI organizations. This work addresses the social and organizational factors that influence alignment outcomes.
---------------------------------------------------------------------
AI Safety Career Development: Score (0/10)
Programs and resources helping talented individuals pursue careers in AI safety. These efforts build human capital dedicated to alignment challenges.
---------------------------------------------------------------------
Industry Safety Pledges and Standards: Score (0/10)
Development of voluntary safety commitments and standards for AI research organizations. These initiatives establish baseline safety practices across the field.
Formal Verification for AI Systems
Total Score (2.1/10)
Total Score Analysis: Formal verification scores moderately on impact (6/10) with potential for higher impact as techniques mature. Feasibility (5/10) faces substantial technical challenges. Uniqueness (6/10) reflects novel verification approaches. Scalability (5/10) is moderate due to computational complexity challenges. Sustainability (6/10) is good through formal guarantees. Auditability (9/10) is excellent by definition. It moderately reduces p(doom) (-6/10) by providing guarantees about system behavior. Cost efficiency (-5/10) reflects substantial research investment required.
---------------------------------------------------------------------
Description: Developing mathematical proofs and verification techniques to guarantee specific safety properties in AI systems.
---------------------------------------------------------------------
DeepMind's Specification and Verification: Score (0/10)
Research program developing formal methods to verify properties of neural networks. Their work addresses how to mathematically guarantee certain behaviors in complex AI systems.
---------------------------------------------------------------------
Verified AI Research: Score (0/10)
Academic and industry collaboration developing verification tools for machine learning systems. Their approach combines traditional formal methods with novel techniques for neural networks.
---------------------------------------------------------------------
Carnegie Mellon Model Checking: Score (0/10)
Research extending model checking techniques to AI systems. This work adapts proven verification methods from traditional software to the challenges of machine learning.
C
Hardware-Based Safety Measures
Total Score (2.2/10)
Total Score Analysis: Hardware-based safety measures score moderately on impact (5/10) as final layers of defense. Feasibility (6/10) is moderate with proven hardware security techniques. Uniqueness (5/10) reflects standard security approaches. Scalability (4/10) faces challenges with increasingly capable AI. Sustainability (5/10) is moderate as hardware constraints may be circumvented. Auditability (7/10) is good through physical inspection. It somewhat reduces p(doom) (-4/10) by providing additional safety layers. Cost efficiency (-4/10) reflects substantial implementation costs.
---------------------------------------------------------------------
Description: Physical and hardware-level constraints, monitoring, and control systems for advanced AI.
---------------------------------------------------------------------
Physical Containment Research: Score (0/10)
Studies on how to physically isolate advanced AI systems from networks and physical actuators. These approaches provide last-resort protections against unaligned systems.
---------------------------------------------------------------------
Compute Governance Mechanisms: Score (0/10)
Research on controlling access to the computational resources necessary for advanced AI development. These approaches aim to prevent uncoordinated or unsafe development.
AI Ethics Frameworks
Total Score (2.7/10)
Total Score Analysis: AI ethics frameworks score relatively low on direct ASI alignment impact (4/10) but provide important foundations. Feasibility (8/10) is high with numerous frameworks already developed. Uniqueness (4/10) is low with substantial overlap between frameworks. Scalability (5/10) is moderate as principles may not scale to ASI capabilities. Sustainability (6/10) is good through institutional adoption. Auditability (6/10) is moderate through ethical review processes. It minimally reduces p(doom) (-3/10) without technical implementation. Cost efficiency (-2/10) is very good for the foundation it provides.
---------------------------------------------------------------------
Description: Ethical guidelines and principles for AI development and deployment, including fairness, accountability, and transparency.
---------------------------------------------------------------------
Partnership on AI Ethics Guidelines: Score (0/10)
Collaborative development of ethical frameworks for AI research and deployment. These guidelines establish baseline ethical considerations for AI development.
---------------------------------------------------------------------
UNESCO AI Ethics Framework: Score (0/10)
International framework for ethical AI development endorsed by member states. This approach provides global ethical standards that can inform regulation.
AI Alignment Benchmarks and Evaluation
Total Score (5.4/10)
Total Score Analysis: Alignment benchmarks score moderately on impact (5/10) by enabling empirical evaluation. Feasibility (7/10) is good for current AI capabilities but faces challenges for ASI. Uniqueness (6/10) reflects novel evaluation approaches. Scalability (4/10) is limited by the difficulty of evaluating superintelligent systems. Sustainability (6/10) is good through benchmark evolution. Auditability (8/10) is excellent through standardized measures. It somewhat reduces p(doom) (-4/10) by highlighting alignment failures. Cost efficiency (-3/10) is good for the value provided.
---------------------------------------------------------------------
Description: Standardized tests, metrics, and evaluation frameworks to measure progress in AI alignment research.
---------------------------------------------------------------------
Alignment Evaluation Research: Score (0/10)
Development of metrics and benchmarks for measuring alignment progress. These tools help quantify and compare different alignment approaches.
---------------------------------------------------------------------
AI Incident Database: Score (0/10)
Collection and analysis of AI failure modes and alignment-related incidents. This resource provides empirical data on alignment challenges.