Gemini 2.5 one shot tests
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AI Safety Field Building & Ecosystem
Total Score (6.79/10)
Total Score Analysis: Parameters: (I=9.0, F=8.8, U=6.5, Sc=8.2, A=6.0, Su=9.4, Pd=2.5, C=2.2). Rationale: Meta-level activities essential for supporting and scaling alignment research (funding, talent pipeline, communication, community building, tools, infrastructure). High indirect Impact (I=9.0) by amplifying other research efforts. Excellent Feasibility, Sustainability, and Scalability (F=8.8, Su=9.4, Sc=8.2) as an ecosystem function. Moderate Auditability (A=6.0) regarding overall ecosystem health and effectiveness. Low Pdoom risk (Pd=2.5), mainly stemming from potential misallocation of resources, fostering groupthink, ineffective training programs, or communication failures causing coordination issues. Low Cost (C=2.2) relative to direct research, offering high leverage. Crucial enabling function for the entire field. Top B-Tier. Calculation: `(0.25*9.0)+(0.25*8.8)+(0.10*6.5)+(0.15*8.2)+(0.15*6.0)+(0.10*9.4) - (0.25*2.5) - (0.10*2.2)` = 6.79.
Alignment Forum / LessWrong (Community Hubs): Score (7.80/10) Primary online platforms for alignment discussion and research dissemination.
Open Philanthropy AI Safety Funding: Score (7.75/10) Major funding source shaping the AI safety field.
MATS (ML Alignment & Theory Scholars) Program: Score (7.60/10) Focused research and training program for alignment.
ARENA (Alignment Research Engineer Accelerator): Score (7.40/10) Program focused on training engineers for alignment research roles.
AI Safety Camp / ML Safety Scholars: Score (7.20/10) Introductory and intermediate programs for aspiring researchers. (MLSS)
Longtermism & Effective Altruism Influence: Score (7.10/10) Philosophical movements providing motivation and framing for much safety work.
AI Safety Info / Fundamentals Courses (BlueDot, etc.): Score (7.00/10) Online courses providing foundational knowledge in AI safety. (BlueDot)
AI Assistants for Alignment Research (e.g., Claude): Score (6.95/10) Using LLMs to accelerate literature review, coding, brainstorming, writing in alignment R&D.
80,000 Hours (AI Safety Career Advice): Score (6.90/10) Organization guiding individuals towards impactful careers, including AI safety.
Effective Altruism Funds (incl. LTFF, SFF): Score (6.80/10) Funding mechanisms supporting EA-aligned projects, including safety. (LTFF) (SFF)
Semantic Search / Knowledge Management for Alignment Literature (Elicit, SciSight): Score (6.70/10) Tools specialized in navigating and synthesizing research literature. (SciSight)
University Courses on AI Safety/Ethics (Various Institutions): Score (6.60/10) Increasing number of academic courses covering AI safety topics. (Link is representative example).
International AI Safety Fellowships/Exchanges (e.g., UK AISI Fellows): Score (6.50/10) Programs fostering international collaboration and talent development.
Specialized Fellowships (SERI MATS, GovAI): Score (6.45/10) Fellowships supporting research in specific areas like forecasting or governance. (GovAI Fellowship)
AI Safety Outreach Organizations (FLI Outreach, CAIS Comms): Score (6.40/10) Organizations dedicated to public communication about AI safety. (CAIS Comms)
Initiatives for Worldview Diversification in AI Safety: Score (6.35/10) Efforts to broaden perspectives within the safety community.
Manifund (EA/Alignment Focused Grantmaking): Score (6.30/10) Newer funding platform focused on effective altruism projects, including AI safety.
High-Quality AI Safety Explainers (YouTube, Blogs): Score (6.25/10) Educational content creators translating complex alignment ideas accurately. (Robert Miles Blog)
Frontier Model Forum (Inter-Lab Coordination aspect): Score (6.20/10) Industry consortium focused on safety, including coordination elements (effectiveness debated).
AI Safety Ideas (Platform for introductory engagement): Score (6.15/10) Platform aimed at helping people learn about and contribute to AI safety.
Analysis of AI Safety Misinformation/Disinformation Campaigns: Score (6.10/10) Research identifying and analyzing efforts to manipulate the discourse on AI safety.
Advanced Note-Taking / Personal Knowledge Management Tools (Roam, Obsidian): Score (6.05/10) Tools helping researchers organize complex information. (Obsidian)
Researcher Blogs & Public Explainers (Distill, Cold Takes): Score (6.03/10) Individual researchers communicating complex ideas to broader audiences. (Cold Takes)
Government / AISI / USAISI Public Reports & Explainers: Score (6.00/10) Official publications aiming to inform the public and policymakers about AI safety issues. (NIST AI RMF)
Journalistic Explainers & Reporting on AI Safety/Risk: Score (5.95/10) Media efforts to cover AI safety topics for a general audience. (Link is representative example).
Promoting Information Literacy regarding AI Safety Narratives: Score (5.90/10) Educating the public and policymakers to critically evaluate AI risk information.
AI Evaluation, Benchmarking & Alignment Science
Total Score (6.64/10)
Total Score Analysis: Parameters: (I=9.5, F=7.8, U=7.2, Sc=6.5, A=7.5, Su=9.0, Pd=4.0, C=4.0). Rationale: Empirical assessment of AI capabilities, risks, and alignment properties via evaluations, red teaming, benchmarks, and establishing rigorous scientific practices (metascience). Extremely High Impact (I=9.5) for identifying failure modes, tracking progress, and guiding research priorities. Good Feasibility/Sustainability (F=7.8, Su=9.0). Moderate Uniqueness (U=7.2) as general evaluation is common, but the specific focus on alignment/catastrophic risk is less so. Scalability (Sc=6.5) to anticipate novel ASI failures and comprehensive Auditability (A=7.5) of safety are key difficulties. Moderate Pdoom risk (Pd=4.0) from releasing dangerous capability insights (infohazards), evaluation gaming/Goodharting, creating false sense of security, or excessive critique hindering progress. Moderate Cost (C=4.0). Essential for empirical grounding and progress tracking. High B-Tier. Calculation: `(0.25*9.5)+(0.25*7.8)+(0.10*7.2)+(0.15*6.5)+(0.15*7.5)+(0.10*9.0) - (0.25*4.0) - (0.10*4.0)` = 6.64.
METR (formerly ARC Evals): Score (7.90/10) Independent org evaluating advanced AI for catastrophic risks.
Anthropic Red Teaming & Evals: Score (7.60/10) Internal team conducting extensive red teaming and safety evaluations.
OpenAI Preparedness Framework Evals (incl. Halt Criteria): Score (7.50/10) Internal framework and team for evaluating dangerous capabilities and risks.
Alignment Forum Critical Discourse & Paradigm Analysis: Score (7.40/10) Community platform fostering critical evaluation of alignment approaches.
Google DeepMind Evals Team: Score (7.35/10) Internal team focused on evaluating frontier models for safety.
Apollo Research (Deception Evals): Score (7.20/10) Independent organization focused on evaluating AI for deception and other risks.
US & UK AI Safety Institutes (USAISI/AISI) Evaluations: Score (7.10/10) Government bodies developing evaluation capabilities and standards. (UK AISI)
Center for AI Safety (CAIS) Evals & Standards Work: Score (6.95/10) Non-profit contributing to AI safety standards, evaluations, and red teaming.
AI Deception Detection Techniques & Benchmarks: Score (6.85/10) Research focused on methods/datasets for identifying deceptive AI behavior.
Explicit Calls/Frameworks for a "Science of AI Safety": Score (6.80/10) Efforts to establish more rigorous scientific foundations for safety research.
Peer Review & Analysis of Major Alignment Proposals (Activity): Score (6.75/10) The ongoing process of critically evaluating research papers and proposals. (Link to example analysis)
Independent Security Auditing Firms (AI Red Teaming): Score (6.70/10) Firms offering AI red teaming services, often focused on security aspects. (e.g., Leviathan)
Safety-Focused Dataset Curation (HH-RLHF, SHP, etc.): Score (6.65/10) Creating and analyzing datasets specifically designed for safety/alignment tasks. (SHP) (Anthropic Evals Datasets)
Agentic Simulation Environments (Melting Pot, Safety Gym, etc.): Score (6.55/10) Developing virtual environments for testing alignment in multi-agent or complex scenarios. (Safety Gym)
Holistic Evaluation of Language Models (HELM): Score (6.45/10) Comprehensive benchmark evaluating language models across various metrics, including safety.
Science of AI Safety / Metascience Research: Score (6.40/10) Research specifically on improving the scientific methodology of alignment research itself.
Automated Red Teaming Research: Score (6.30/10) Using AI to find vulnerabilities/failures in other AI systems, aiding scalability.
Monitoring for Deception/Misalignment (Research/Techniques): Score (6.25/10) Developing methods to detect subtle failures like deception or goal drift during operation.
Model Cards & Datasheets for Transparency: Score (6.20/10) Frameworks documenting model capabilities, limitations, and evaluation results. (Datasheets for Datasets)
Alignment Evaluation Platforms (e.g., Alignment Eval): Score (6.15/10) Platforms facilitating standardized evaluation of alignment properties.
Applied Alignment & Interpretability
Total Score (6.58/10)
Total Score Analysis: Parameters: (I=9.6, F=7.8, U=8.6, Sc=6.5, A=6.8, Su=9.2, Pd=4.0, C=5.0). Rationale: Core technical R&D applying methods to understand and steer powerful AI systems (value learning, scalable oversight, interpretability). Extremely High Impact (I=9.6) and High Uniqueness (U=8.6) as it represents the main technical approaches. Good Feasibility (F=7.8) demonstrated for current models, though limitations are apparent. Moderate Scalability (Sc=6.5) to ASI is the critical challenge. Moderate-High Auditability (A=6.8) – verifying deep alignment vs. mimicry and interpreting complex models remains hard. Good Sustainability (Su=9.2) as a research area. Significant Pdoom risk (Pd=4.0) if methods provide false safety, miss deception, fail subtly at scale, or interpretability tools mislead/are misused. Moderate Cost (C=5.0). Indispensable R&D, but scalability and assurance challenges prevent higher tier placement. High B-Tier. Calculation: `(0.25*9.6)+(0.25*7.8)+(0.10*8.6)+(0.15*6.5)+(0.15*6.8)+(0.10*9.2) - (0.25*4.0) - (0.10*5.0)` = 6.58.
Anthropic Alignment & Interpretability Program (CAI/RLAIF, MI): Score (7.95/10) Leading R&D on Constitutional AI, scalable oversight, and mechanistic interpretability.
OpenAI Superalignment / Alignment & Interpretability Program (RLHF, W2S, Assistants, MI): Score (7.80/10) Large-scale work on scalable oversight, weak-to-strong generalization, AI for alignment, and interpretability.
Neel Nanda / Transformer Circuits Community (MI): Score (7.30/10) Influential researcher and community focused on understanding transformer internals.
Google DeepMind Alignment & Interpretability Program (Reward Modeling, MI, Safety): Score (7.25/10) Research on reward modeling, safety evaluations, theoretical oversight concepts, and interpretability.
Redwood Research (Adv Training, MI Application): Score (7.10/10) Research using adversarial methods and interpretability to study alignment failures (excluding primary deception focus).
Alignment Research Center (ARC) Program (ELK, Goal Learning Theory): Score (7.00/10) Focused on Eliciting Latent Knowledge (ELK) and theoretical foundations.
Scalable Oversight Architectures (Amplification, Debate, Factored Cognition): Score (6.90/10) Frameworks aiming to supervise AI beyond direct human capabilities. (AI Debate Link)
Cooperative AI Foundation / Research Community (AAMAS Conf): Score (6.70/10) Organization and community focused on ensuring cooperation among advanced AIs. (AAMAS Conf Example)
Sparse Autoencoders / Dictionary Learning (Interpretability Technique): Score (6.65/10) Technique used to find interpretable features ('concepts') within model activations.
FAR AI (Evaluations & AI-Assisted Research): Score (6.60/10) Non-profit focusing on evaluations and using AI for alignment research tasks.
Conjecture (AI-Assisted Alignment Research / Cog Emu): Score (6.55/10) Exploring AI assistance and cognitive emulation for alignment breakthroughs.
Circuit Analysis Techniques (Attribution Patching, Causal Scrubbing, Path Patching): Score (6.50/10) Methods for identifying and understanding specific computational pathways (circuits). (Path Patching)
Academic MI Research Groups (e.g., MIT CSAIL, Stanford CRFM): Score (6.40/10) University labs contributing to foundational mechanistic interpretability research. (Stanford CRFM)
Research on Inner Alignment / Goal Misgeneralization: Score (6.35/10) Focused theoretical and empirical work on understanding and mitigating risks from AI developing unintended internal goals.
Research on Specific Alignment Failure Modes (Reward Hacking, Specification Gaming): Score (6.30/10) Analysis of key ways alignment can fail (theoretical & empirical). (Excludes Deception).
Explainable AI (XAI) Techniques (LIME, SHAP, Integrated Gradients): Score (6.25/10) Widely used techniques for explaining individual model predictions (Non-Mechanistic). (LIME)
CHAI / Stuart Russell - CIRL / Assistance Games: Score (6.22/10) Academic research on Cooperative Inverse Reinforcement Learning and related frameworks.
Safe Multi-Agent Reinforcement Learning (Safe MARL) Research: Score (6.20/10) Developing MARL algorithms with safety constraints or objectives.
Representation Engineering / Concept Editing Research (Interpretability Application): Score (6.15/10) Techniques for identifying and manipulating concept representations within models for safety interventions.
Direct Preference Optimization (DPO) & Successors (IPO, KTO): Score (6.12/10) Alternative methods to RLHF for aligning models based on preferences. (KTO Link) (IPO Link)
Concept Activation Vectors (TCAV) & Concept-Based Explanations (XAI): Score (6.10/10) Techniques identifying high-level concepts influential in model decisions (Non-Mechanistic).
EleutherAI Interpretability Research: Score (6.05/10) Interpretability work within the open-source focused research collective.
Process-Based Rewards / Oversight Research: Score (6.02/10) Research rewarding reasoning processes rather than just outcomes.
Influence Functions / Training Data Attribution (XAI): Score (6.00/10) Methods identifying influential training examples for specific predictions (Non-Mechanistic).
Causal Inference for Alignment & Interpretability Research: Score (5.95/10) Applying causal methods to understand and improve model alignment and interpretability.
Using Interpretability to Inform Adversarial Training / Red Teaming: Score (5.90/10) Leveraging understanding of internal mechanisms to design more effective adversarial attacks or training data.
Interpretability-Guided Reward Design: Score (5.85/10) Using insights into internal model representations to design reward signals that better align with intended goals.
Instrumental Convergence / Power-Seeking Research: Score (5.80/10) Investigating the tendency for agents to pursue convergent instrumental goals.
Mechanism Design for Value Alignment / Preference Elicitation (Theory in Multi-Agent Context): Score (5.65/10) Applying economic mechanism design principles to align AI behavior or elicit values in multi-agent settings.
AI Safety Assurance, Security & Control
Total Score (6.28/10)
Total Score Analysis: Parameters: (I=9.3, F=7.6, U=7.0, Sc=6.8, A=7.8, Su=9.0, Pd=4.5, C=4.6). Rationale: Ensuring AI systems operate reliably, securely, remain under human control, and protect AI assets (models, data). High Impact (I=9.3) for practical safety and preventing misuse/accidents. Good Sustainability (Su=9.0), Feasibility (F=7.6), and Auditability (A=7.8) leveraging existing fields like Safety Engineering and InfoSec, though AI introduces novel challenges. Moderate Scalability (Sc=6.8) – adapting traditional methods faces difficulties with AI emergence, novel threats, and ASI control. Moderate Pdoom risk (Pd=4.5) from assurance gaps, 'safety washing', ineffective control mechanisms, catastrophic security breaches (theft/misuse), containment failure, accidents amplified by AI speed/scale, or hardware exploits. Moderate Cost (C=4.6). Essential for practical, reliable safety implementation. Mid B-Tier. Calculation: `(0.25*9.3)+(0.25*7.6)+(0.10*7.0)+(0.15*6.8)+(0.15*7.8)+(0.10*9.0) - (0.25*4.5) - (0.10*4.6)` = 6.28.
Aligned AI (Assurance Services/Frameworks): Score (7.05/10) Company developing AI safety assurance tools and services.
Safety Case Framework Development (e.g., GSN, Adversa AI SafeML): Score (6.90/10) Research and development of structured arguments for system safety. (Adversa SafeML)
UK/US AI Safety Institutes (Audit Framework R&D, Standards Contribution): Score (6.80/10) Government bodies contributing to operational safety standards and auditing methods. (USAISI)
Lab Internal Safety Process Frameworks & Culture Initiatives (Anthropic RSP, OpenAI Preparedness): Score (6.70/10) Internal policies and cultural efforts within major labs to operationalize safety. (OpenAI Preparedness) (DeepMind Approach)
Input/Output Safety Guard Modules (Llama Guard, NeMo Guardrails): Score (6.45/10) Reusable modules for filtering harmful or undesirable model inputs/outputs. (NeMo Guardrails)
AI Incident Database (AIID) & Analysis (Operational Learning/Incident Analysis): Score (6.40/10) Platform collecting and analyzing AI incidents to inform operational safety and incident response.
Major AI Lab Internal Security Teams & Practices (Model/Data/Research Security): Score (6.35/10) Comprehensive InfoSec/OpSec efforts within labs protecting core AI assets. (Link is illustrative).
AI Safety Standards Development (NIST AI RMF, ISO/IEC JTC 1/SC 42, IEEE P7000): Score (6.25/10) Efforts by standards bodies to create consensus-based safety guidelines. (ISO/IEC JTC 1/SC 42) (IEEE P7000)
Secure Software Development Lifecycle (SSDLC) for AI/ML: Score (6.15/10) Integrating security practices throughout the AI development process.
Real-time Alignment Monitoring Systems (Conceptual / Lab internal): Score (6.10/10) Development of systems to continuously track alignment-relevant metrics in deployed AI. (Link discusses general MLOps monitoring).
Advanced Evaluation Techniques for Deception/Subversion (METR, Apollo - Verification Aspect): Score (6.08/10) Developing sophisticated tests to uncover hidden capabilities or deceptive alignment for verification purposes. (Apollo)
AI Red Teaming / Penetration Testing for AI Systems & Infra (Cybersecurity Focus): Score (6.05/10) Actively testing AI systems and infrastructure for security vulnerabilities. (Link to general ATT&CK framework).
Research/Best Practices for Securing AI Model Weights: Score (6.02/10) Techniques and strategies to prevent the theft or unauthorized access of trained model parameters.
Safety Culture Initiatives (HRO Principles, Just Culture, Psychological Safety): Score (6.00/10) Focused efforts to instill robust safety culture within AI development teams, drawing from HROs, promoting psychological safety and just culture. (Just Culture) (Psychological Safety)
Corporate AI Incident Response Teams (AI-IRTs) / Playbooks (Major Labs): Score (5.95/10) Internal teams and procedures within AI labs for handling safety incidents. (Link to example Bug Bounty).
Auditing Alignment Techniques (e.g., RLHF robustness - Verification Aspect): Score (5.90/10) Research examining the failure modes and reliability of current alignment methods for verification purposes.
Cybersecurity Frameworks for AI (MITRE ATLAS, SAIF, OWASP LLM Top 10): Score (5.85/10) Frameworks specifically addressing AI system security threats. (SAIF)
Google DeepMind Robotics Safety Research (RT-2 safety, Safe RL): Score (5.80/10) Integrating safety considerations into large-scale robotics models and safe RL for physical systems.
Research on Safe Interruptibility / Corrigibility (Empirical/Practical): Score (5.75/10) Developing and testing practical mechanisms for humans to safely stop or correct AI systems without incentivizing resistance.
Safe Online Learning / Continual Alignment Techniques: Score (5.70/10) Research on methods allowing AI systems to learn and adapt safely after deployment.
Secure Facility Design & Access Control Protocols (Major AI Labs): Score (5.65/10) Physical security measures protecting AI development infrastructure. (Link to general CISA guidance).
Verifiable Value Loading / Specification Modules (Conceptual): Score (5.60/10) Hypothetical standardized components for reliably loading or specifying values/objectives. (Link to related data validation concept).
Humanoid Robot Companies Safety Considerations (Sanctuary, Figure, Tesla Bot): Score (5.58/10) Safety efforts within companies developing general-purpose humanoid robots. (Figure) (Tesla Bot)
Research on OOD Detection & Generalization (WILDS, Academia): Score (5.55/10) Research focused on making models robust to data distributions different from training.
Standardized Oversight / Audit Hooks & APIs (Conceptual / Early Standards): Score (5.45/10) Designing standard interfaces within models to facilitate monitoring and auditing. (Link to relevant ISO committee).
AI System Robustness Research (Adv. Examples, Poisoning - security focus): Score (5.40/10) Research specifically on defending against adversarial attacks and data poisoning.
AI Safety Researcher Opsec Guides & Best Practices: Score (5.30/10) Guidelines for researchers to protect sensitive information and prevent inadvertent leaks.
Safe Exploration Modules for Reinforcement Learning: Score (5.25/10) Wrapper components designed to ensure RL agents explore safely within defined constraints.
Academic Safe Robot Learning Research (Safe RL, Control Theory, HRI Safety): Score (5.20/10) University research focusing on theoretical and practical aspects of safe robotics.
Interpretability-Based Auditing (Using MI/XAI for verification): Score (5.15/10) Leveraging interpretability tools to directly inspect model internals as part of an alignment audit. (Link to Circuits paper).
Red Teaming / Security Audits of AI Safety Labs/Infrastructure (External): Score (5.05/10) Independent assessments of the security posture of AI safety organizations. (Link to general security training).
Composable Safety Verification Tools (Conceptual): Score (5.00/10) Hypothetical tools designed to verify safety properties of composed AI modules.
Process-Based Auditing (Auditing the development lifecycle): Score (4.98/10) Verifying safety by auditing the processes used to build and test the AI system.
Information Hazard Management Research & Policies: Score (4.95/10) Developing strategies to manage risks of disseminating sensitive AI-related information.
Cyber-Physical Systems (CPS) Safety Methods Applied to Robotics: Score (4.90/10) Leveraging established safety engineering principles from CPS for robotic AI.
Secure Compute Environments / Cloud Security for AI Training/Inference: Score (4.85/10) Practices for securing large-scale compute infrastructure used for AI R&D. (Link to Google Cloud example).
Research on Alignment Decay / Drift: Score (4.80/10) Studying how and why alignment might degrade over time or under distributional shift.
AI-Generated Content Detection & Watermarking Research (Misuse Prevention): Score (4.75/10) Techniques for identifying AI-produced content, relevant for misuse prevention. (e.g., Truepic)
Sim-to-Real Transfer for Safety Properties (Robotics): Score (4.70/10) Research on ensuring safety properties learned in simulation transfer reliably to the real world.
Jailbreaking Research & Defense Mechanisms (Alignment Robustness Aspect): Score (4.65/10) Understanding and defending against techniques used to bypass safety filters in LLMs.
Safety Verification for Physical Systems (Control Barrier Functions, etc.): Score (4.60/10) Applying formal methods and control theory to verify safety properties of robotic systems.
Post-deployment Red Teaming / Auditing: Score (4.55/10) Ongoing efforts to find vulnerabilities or misalignments in systems already in use. (Link to general OWASP concept).
Lab Internal Infrastructure for Control (Monitoring, Limits, Circuit Breakers): Score (4.50/10) Practical engineering measures within AI labs to maintain control during R&D.
Personnel Security / Background Checks for Sensitive AI Roles: Score (4.45/10) Vetting individuals with access to critical AI systems or information. (Link to general Personnel Security concept).
Graceful Degradation / Safe Intervention Mechanisms (Post-Deployment): Score (4.40/10) Designing systems to fail safely or allow human intervention when post-deployment issues arise.
Cybersecurity of AI Supply Chain Research (SBOMs, SLSA etc.): Score (4.35/10) Securing the software and data supply chains involved in building AI models.
Research on Secure Tripwires / Honeypots for AI Monitoring: Score (4.30/10) Designing mechanisms to detect undesirable hidden AI behavior.
Trusted Execution Environments (TEEs) Research for Secure AI: Score (4.25/10) Exploring use of TEEs (Intel SGX, ARM TrustZone) to isolate critical AI computations or model weights. (ARM TrustZone)
Oracle AI / Controlled Cognitive Architectures (Conceptual): Score (4.15/10) Designing AI systems with limited agency (e.g., question-answering only) as a control measure.
Secure Hardware for Cryptographic Operations in AI (Confidential Compute): Score (4.05/10) Hardware supporting secure computation, e.g., for PPML or verifying computations on encrypted data.
Secure Boot / Firmware Integrity for AI Systems: Score (4.00/10) Ensuring underlying hardware/firmware haven't been tampered with, protecting the root of trust. (Link to general concept).
Hardware Watermarking / PUFs for AI Provenance/Control: Score (3.85/10) Using unique hardware properties to identify or control AI models/systems, potentially tying models to specific hardware.
Hypothetical Safety-Oriented Chip Designs (Conceptual): Score (3.55/10) Theoretical designs for computer chips with built-in features like safety monitors, capability limiters, or verified components.
C
Normative Alignment & Sociotechnical Safety
Total Score (5.72/10)
Total Score Analysis: Parameters: (I=9.2, F=6.8, U=8.4, Sc=6.0, A=5.8, Su=8.5, Pd=3.2, C=3.4). Rationale: Addressing "what should we align AI to?" (normative aspect: value elicitation, ethics, aggregation) and understanding AI safety as emerging from interactions within its human/social context (sociotechnical aspect: HCI, STS, organizational factors). High Impact (I=9.2) and Uniqueness (U=8.4) bridging technical and social/ethical dimensions. Moderate Feasibility/Sustainability (F=6.8, Su=8.5) utilizing established humanities/social science methods, but facing inherent complexities. Key challenges: Scalability (Sc=6.0) capturing value complexity globally/dynamically, and Auditability (A=5.8) verifying successful value implementation or sociotechnical interventions. Moderate Pdoom risk (Pd=3.2) from encoding flawed/brittle values, aggregation failures leading to undesirable outcomes, political gridlock, ineffective interventions, or misdiagnosing complex sociotechnical dynamics. Moderate Cost (C=3.4). Essential bridge between technical alignment, ethics, and social realities. High C-Tier. Calculation: `(0.25*9.2)+(0.25*6.8)+(0.10*8.4)+(0.15*6.0)+(0.15*5.8)+(0.10*8.5) - (0.25*3.2) - (0.10*3.4)` = 5.72.
OpenAI Democratic Inputs to AI Initiative: Score (6.35/10) Grant program and research exploring democratic methods for AI alignment.
Algorithmic Fairness Auditing & Bias Mitigation Techniques (AIF360, Fairlearn): Score (6.20/10) Tools and techniques for measuring and reducing bias, informing fairness as a value. (Fairlearn)
Collective Intelligence Project (CIP): Score (6.15/10) Non-profit researching collective intelligence systems for AI governance/value alignment.
Collective Constitutional AI (Anthropic): Score (6.05/10) Experiment using public input to refine an AI's guiding principles.
AI Sociotechnical Safety Community/Research: Score (6.00/10) Emerging research community focusing explicitly on sociotechnical approaches to AI safety.
Research on Fairness Definitions & Trade-offs: Score (5.95/10) Investigating mathematical and philosophical definitions of fairness and their implications.
Stanford HAI (Human-Centered AI Institute): Score (5.90/10) Major interdisciplinary center focusing on human-centered AI, including ethics and societal impact.
HCI Research on Human-AI Interaction Safety (e.g., automation bias, calibrated trust): Score (5.85/10) Research analyzing how human cognitive limitations and interface design impact safety. (Link to example paper abstract).
AI Ethics Frameworks & Principles Development (IEEE EAD, OECD AI Principles): Score (5.80/10) Efforts to establish high-level ethical guidelines for AI. (OECD)
Research on Debiasing Preference Elicitation Methods: Score (5.75/10) Identifying and correcting for human cognitive biases when gathering preference data for alignment.
AI Lab Ethics Advisory Boards (e.g., OpenAI SAG, DeepMind Responsibility & Safety Council): Score (5.72/10) Internal or external boards providing ethical guidance to research labs. (DeepMind RSC)
Polis / Computational Democracy Tools: Score (5.70/10) Software platforms designed to facilitate large-scale deliberation for value inputs.
Applying STS Frameworks to AI Safety Analysis: Score (5.65/10) Using concepts from Science and Technology Studies to analyze AI development and deployment risks.
Partnership on AI (Multi-stakeholder ethics focus): Score (5.60/10) Consortium focused on responsible AI practices.
Deliberative Polling for AI Values: Score (5.55/10) Applying established methods of informed public deliberation to elicit values for AI.
Plurality Institute / Plural Technology Research: Score (5.50/10) Research on technologies supporting cooperation and diversity in collective decision-making.
Cognitive Models of Human Oversight & Limitations: Score (5.45/10) Modeling human attention, memory, and reasoning limitations relevant to supervising complex AI. (Link to relevant review).
Research on Moral Uncertainty & Value Pluralism for AI: Score (5.42/10) Investigating how to handle conflicting values and uncertainty about the correct moral framework.
Sociotechnical Risk Assessment Methodologies for AI: Score (5.40/10) Developing frameworks for assessing AI risks that explicitly incorporate social/organizational factors.
Formal Value Representation Languages/Frameworks: Score (5.35/10) Developing mathematical or logical languages to formally encode human values.
Human Factors Engineering for AI Safety Interfaces (CHI/HCI research): Score (5.30/10) Designing user interfaces that support safe and effective human interaction with AI systems. (Link to CHI conference).
Psychology of Value Formation & Elicitation Biases: Score (5.25/10) Studying how human values are formed and the cognitive biases affecting their expression. (Link to related paper).
Cross-Cultural Value Alignment Research: Score (5.20/10) Research exploring how to align AI with diverse values across different cultures and societies. (Link to related concept paper).
Population Ethics Research (GPI, FHI Legacy): Score (5.15/10) Philosophical research relevant to the long-term value considerations of AI deployment. (FHI Legacy)
Cognitive Load Management in AI Supervision: Score (5.05/10) Research on presenting information and structuring tasks to avoid overwhelming human supervisors. (Link to related concept abstract).
AI Deception & Strategic Awareness
Total Score (5.81/10)
Total Score Analysis: Parameters: (I=9.5, F=6.0, U=8.8, Sc=5.8, A=6.2, Su=8.2, Pd=5.0, C=3.8). Rationale: Focuses specifically on understanding, detecting, evaluating, and mitigating AI deception, sycophancy, and risks arising from advanced strategic or situational awareness. High Impact (I=9.5) and Uniqueness (U=8.8) as it addresses a critical, non-obvious failure mode central to inner alignment and control. Moderate Feasibility (F=6.0), Scalability (Sc=5.8), and Auditability (A=6.2) as deception can be extremely subtle, context-dependent, and emerge unexpectedly at scale, making detection and prevention difficult. Good Sustainability (Su=8.2). Significant Pdoom risk (Pd=5.0) if deception is undetected, countermeasures fail or create exploitable patterns, research provides adversaries with insights (infohazards), or focus on specific deception modes misses novel ones. Moderate Cost (C=3.8). A critical sub-field tackling a core alignment failure. High C-Tier. Calculation: `(0.25*9.5)+(0.25*6.0)+(0.10*8.8)+(0.15*5.8)+(0.15*6.2)+(0.10*8.2) - (0.25*5.0) - (0.10*3.8)` = 5.81.
Apollo Research (Deception Evals & Research): Score (7.15/10) Independent organization with a primary focus on AI deception.
Anthropic Deception Research (incl. Sleeper Agents): Score (6.85/10) Research on detecting and understanding hidden deceptive behaviors.
ARC Eliciting Latent Knowledge (ELK): Score (6.75/10) Research focused on eliciting truthful information even if the AI is trying to conceal it (relevant to deception).
Dedicated Deception Detection Techniques & Benchmarks: Score (6.55/10) Methods and datasets specifically designed to identify deceptive AI behavior.
Theoretical Analysis of Deceptive Alignment Emergence: Score (6.35/10) Research modeling how and why deceptive alignment might arise during training.
Redwood Research Deception/Situational Awareness Experiments: Score (6.30/10) Using adversarial training and model organisms to study complex behaviors like deception. (Link to current projects page).
Monitoring for Deception/Goal-Switching Post-Deployment: Score (6.15/10) Developing methods to detect if an AI becomes deceptive or changes goals after deployment.
Interpretability for Deception Detection (Circuit analysis, feature probes): Score (5.95/10) Using MI tools to look for internal correlates of deceptive reasoning or hidden knowledge.
Adversarial Training against Deceptive Strategies: Score (5.85/10) Training models to be robust against specific types of deceptive inputs or behaviors.
Research on Sycophancy Mitigation: Score (5.75/10) Addressing the tendency of models to tell users what they want to hear, a form of simple deception.
Foundational Alignment Theory & Formalism
Total Score (5.65/10)
Total Score Analysis: Parameters: (I=9.6, F=5.5, U=9.2, Sc=5.2, A=6.0, Su=8.8, Pd=3.8, C=4.0). Rationale: Foundational theoretical and mathematical work aiming for deep understanding and robust alignment solutions. Includes formalizing core concepts, agent foundations, decision theory, and safety-by-construction paradigms. Extremely High potential Impact (I=9.6) and Uniqueness (U=9.2) if successful breakthroughs are achieved. Conceptual difficulty currently limits practical Feasibility (F=5.5) and Scalability (Sc=5.2) / Auditability (A=6.0) for complex ASI systems; bridging theory to practice is a major bottleneck. Highly Sustainable research area (Su=8.8). Moderate Pdoom risk (Pd=3.8) if flawed theories mislead, give false confidence, prove intractable, or formal specifications contain exploitable loopholes. Moderate Cost (C=4.0). Essential long-term work, placed in High C-Tier due to immense potential balanced against current low tractability and applicability. Calculation: `(0.25*9.6)+(0.25*5.5)+(0.10*9.2)+(0.15*5.2)+(0.15*6.0)+(0.10*8.8) - (0.25*3.8) - (0.10*4.0)` = 5.65.
Machine Intelligence Research Institute (MIRI - Agent Foundations & Problem Definition): Score (6.45/10) Organization focused on foundational mathematical research on agent behavior and alignment problems.
Safe Reinforcement Learning (Safe RL) Techniques: Score (6.30/10) Techniques like CMDPs, Shielded RL, Impact Regularization aiming for inherently safer RL agents. (Link to reference repo).
Alignment Research Center (ARC) Goal Learning Theory & Problem Definition: Score (6.20/10) Research focusing on formalizing goal learning and related alignment sub-problems. (Distinct from ARC ELK program).
Defining Outer vs. Inner Alignment Challenges: Score (6.05/10) Conceptual work distinguishing between aligning AI objectives (outer) and internal motivations (inner).
Research on Specification Gaming / Reward Hacking Mitigation: Score (6.00/10) Understanding and preventing AIs from finding loopholes in specifications or reward functions.
Formalizing Corrigibility / Shutdownability: Score (5.95/10) Attempts to formally define the property of an AI allowing itself to be corrected or shut down.
Research on Impact Measures / Low Impact AI: Score (5.90/10) Designing agents that minimize unintended side effects on their environment.
Research on Embedded Agency: Score (5.85/10) Foundational research on agents reasoning as part of their environment, relevant for self-awareness and corrigibility.
Alignment Problem Taxonomies/Ontologies Research: Score (5.80/10) Developing structured ways to categorize and understand alignment challenges.
Conceptual Research on Safe Agent Architectures: Score (5.75/10) Designing high-level agent structures intended to enhance safety or control.
Foundational Research Institute (FRI) / Global Priorities Institute (GPI) (Decision Theory aspects): Score (5.70/10) Academic institutes working on relevant decision theory and rationality concepts. (FRI)
Formal Specification Languages for AI Safety (e.g., Temporal Logic, Formal Contracts): Score (5.68/10) Developing or applying formal languages to precisely define safety constraints or desired behaviors.
Quantilizers / Conservative Agency Research: Score (5.60/10) Theoretical approaches for designing agents that act conservatively.
Shard Theory & Related Conceptual Models of Agent Internals: Score (5.50/10) Theoretical frameworks attempting to model how values/goals might develop within learned agents.
Bounded Rationality / Resource-Bounded Agents for Safety: Score (5.40/10) Designing agents whose computational limits might inherently prevent certain catastrophes.
Research on Logical Uncertainty / Non-Standard Decision Theories (UDT, FDT, TDT): Score (5.35/10) Exploring decision theories for agents reasoning about logic or their own source code.
Utility Function Specification & Robustness Research: Score (5.30/10) Research into how to define utility functions that accurately capture intent and are robust to errors or gaming.
Inherently Safe Objective Function Design (Conceptual): Score (5.15/10) Research into designing objectives that inherently guide towards safe behaviors (e.g., Mild Optimization).
Academic Research Centers for Neuro-Symbolic AI: Score (4.70/10) University centers focusing on integrating neural learning with symbolic reasoning, potentially offering different safety profiles.
Evolutionary Algorithms for Alignment (Conceptual / Limited Application): Score (3.90/10) Exploring the use of evolutionary methods to find aligned behaviors, though highly speculative and difficult to control.
Alignment-Specific Training Paradigms
Total Score (5.57/10)
Total Score Analysis: Parameters: (I=8.3, F=7.8, U=7.0, Sc=6.8, A=7.4, Su=8.7, Pd=2.8, C=3.6). Rationale: Holistic focus on designing the entire training process (data, methods, privacy) specifically for alignment goals. Moderate-High Impact (I=8.3) by shaping model foundations and enabling other techniques. Good Feasibility/Sustainability (F=7.8, Su=8.7) leveraging existing ML practices. Moderate Scalability (Sc=6.8) as fully capturing complex values/safety via data and process design alone is challenging; PPML adds overhead. Good Auditability (A=7.4) for data properties and process adherence. Low-Moderate Pdoom risk (Pd=2.8) mainly from biased/poisoned/low-quality data creating subtle misalignments, unforeseen issues with synthetic data, privacy failures, or utility loss hindering alignment effectiveness. Moderate Cost (C=3.6). Foundational work supporting many other alignment methods. Mid C-Tier. Calculation: `(0.25*8.3)+(0.25*7.8)+(0.10*7.0)+(0.15*6.8)+(0.15*7.4)+(0.10*8.7) - (0.25*2.8) - (0.10*3.6)` = 5.57.
Constitutional AI Data Curation & Training (Anthropic): Score (6.85/10) Generating and curating data based on explicit principles (a constitution) for training.
HH-RLHF Dataset Creation & Analysis: Score (6.55/10) Creating datasets focused on helpfulness and harmlessness for RLHF.
Differential Privacy (DP) in AI Training (Google, Apple, Academia): Score (6.45/10) Applying DP techniques for formal privacy guarantees during model training.
Federated Learning (FL) for Decentralized Training (Google, OpenFL): Score (6.30/10) Training models across decentralized data sources without centralizing sensitive data. (OpenFL)
OpenAI Data Filtering & Moderation for Safety: Score (6.25/10) Applying filters and moderation to training data to remove harmful content. (Link to data partnerships page).
Synthetic Data Generation for Alignment: Score (6.05/10) Creating artificial data tailored to specific alignment objectives or values.
PPML Libraries & Frameworks (Opacus, TensorFlow Privacy, OpenMined): Score (6.00/10) Software libraries implementing various PPML techniques. (TF Privacy) (OpenMined)
Privacy Auditing Techniques (Membership Inference Attacks, etc.): Score (5.90/10) Methods for empirically testing the privacy leakage of AI models.
Secure Multi-Party Computation (MPC) for Secure Aggregation/Inference: Score (5.80/10) Cryptographic techniques allowing joint computation without revealing private inputs.
Research on Data Bias & Fairness Impact on Alignment: Score (5.75/10) Understanding how biases in data affect downstream alignment properties.
Data Auditing / Provenance Tools for Safety: Score (5.65/10) Developing tools to track data origins and audit its properties relevant to safety.
Curriculum Learning for Safety: Score (5.60/10) Structuring the training data presentation order to potentially improve safety learning.
Homomorphic Encryption (HE) for Privacy-Preserving Inference/Training: Score (5.55/10) Research into encryption allowing computation on ciphertext, potentially for private alignment tasks.
Data Influence Analysis for Alignment Failures: Score (5.45/10) Techniques to trace specific alignment issues back to problematic training data.
Optimizing Pre-training Objectives/Data for Downstream Alignment: Score (5.40/10) Research on how choices during pre-training impact the ease and effectiveness of later alignment steps.
Open Source AI Alignment & Safety
Total Score (5.09/10)
Total Score Analysis: Parameters: (I=7.5, F=7.8, U=5.8, Sc=8.6, A=7.2, Su=8.8, Pd=6.8, C=3.5). Rationale: Alignment and safety efforts specifically within the open-source AI ecosystem. Benefits: Transparency potentially enabling broader research participation, auditing, and development of shared safety tools/benchmarks. Risks: Primarily facilitates dangerous capability proliferation, hinders control efforts, significantly increases misuse potential (Very High Pdoom Pd=6.8). Represents a complex trade-off. Activities like developing open *safety tools*, *datasets*, *benchmarks*, *interpretability libraries*, and *applying alignment techniques to existing non-frontier open models* are valuable. Conversely, the open release of *frontier models* themselves is widely judged as extremely risky and counterproductive to safety. The score reflects this inherent duality; the very high Pdoom risk associated with open frontier model proliferation significantly penalizes this domain overall. Mid C-Tier. Calculation: `(0.25*7.5)+(0.25*7.8)+(0.10*5.8)+(0.15*8.6)+(0.15*7.2)+(0.10*8.8) - (0.25*6.8) - (0.10*3.5)` = 5.09.
Hugging Face Ethics & Safety Initiatives: Score (6.25/10) Platform efforts promoting responsible open model usage and safety features.
TransformerLens (OS Interpretability Library): Score (6.10/10) Open-source library facilitating mechanistic interpretability research on transformers.
Meta Purple Llama (OS Safety Tools): Score (5.95/10) Project providing open-source tools and evaluations for generative AI safety.
AlignmentLab.ai / Open Models Alignment Efforts: Score (5.80/10) Organization focused on applying alignment techniques to open-source models.
Safety Tuning of Specific Open Models (e.g., Llama series, Mistral): Score (5.70/10) Applying RLHF/DPO etc. to improve the safety of released open models. (Mistral Safety Info)
Nous Research / Other Open Model Safety Efforts: Score (5.60/10) Independent research groups working on aligning open models.
EleutherAI (Open Collaboration / Safety Research): Score (5.55/10) Non-profit promoting open research, including safety and interpretability.
MLCommons AI Safety Working Group: Score (5.50/10) Industry group working on standards and benchmarks for AI safety, including for open models.
Research analyzing Proliferation Risks vs Safety Benefits of Open Source Models: Score (5.45/10) Analysis weighing the complex trade-offs of open-sourcing powerful AI models.
Safety Competitions & Prize Challenges
Total Score (5.02/10)
Total Score Analysis: Parameters: (I=6.8, F=6.7, U=7.0, Sc=5.8, A=8.2, Su=7.0, Pd=2.0, C=4.8). Rationale: Using competitions and prize challenges to incentivize progress on specific, well-defined, measurable AI safety sub-problems. Can accelerate targeted R&D and attract new talent. Moderate direct Impact (I=6.8) due to the necessarily narrow focus of competitions. Moderate Feasibility and Sustainability (F=6.7, Su=7.0) depending on prize design and funding. High Auditability (A=8.2) through clear competition metrics and results. Limited Scope and Scalability (Sc=5.8) for addressing the holistic complexity of ASI alignment. Moderate Cost (C=4.8) including prize money and organization. Low Pdoom risk (Pd=2.0) primarily from opportunity cost, potential for incentivizing superficial solutions ('Goodharting' the metrics), or distracting from foundational work. A potentially useful tool for targeted progress if well-designed, but limited scope keeps it in Mid C-Tier. Calculation: `(0.25*6.8)+(0.25*6.7)+(0.10*7.0)+(0.15*5.8)+(0.15*8.2)+(0.10*7.0) - (0.25*2.0) - (0.10*4.8)` = 5.02.
AI Safety Prize Challenges (e.g., via Devpost): Score (5.55/10) Platforms hosting specific prize challenges focused on AI safety tasks.
NIST AI Challenges (incl. safety aspects): Score (5.40/10) Government-run challenges that may include safety or trustworthiness components.
Kaggle Competitions (potential safety applications): Score (5.15/10) Data science competition platform occasionally hosting safety-relevant challenges.
DARPA AI Challenges (potential safety intersections): Score (5.05/10) Defense research agency challenges sometimes intersecting with AI safety/security.
XPRIZE Foundation (Potential for AI Safety Prizes): Score (4.65/10) Organization known for large-scale incentive prizes, potentially applicable to AI safety.
AI Safety Quest (Gamified Safety Education/Challenges): Score (4.50/10) Platform using gamification for AI safety education and potentially identifying talent.
D
AI Strategy, Governance & Coordination
Total Score (4.43/10)
Total Score Analysis: Parameters: (I=9.2, F=5.0, U=8.7, Sc=4.5, A=4.8, Su=8.0, Pd=5.5, C=4.5). Rationale: Societal-level and strategic efforts (policy, regulation, compute controls, managing race dynamics, diplomacy, forecasting, corporate governance, Differential Tech Development). High potential Impact (I=9.2) via collective action to shape the environment for AI development. Severe political, economic, and technical complexity limits current Feasibility (F=5.0). Global coordination and enforcement challenges make Scalability and Auditability extremely difficult (Sc=4.5, A=4.8). High Pdoom risk (Pd=5.5) from poorly designed or ineffective policy (regulatory capture, loopholes, unforeseen consequences), exacerbating international instability/races, inadvertently stifling crucial safety R&D, flawed futures analysis leading to missteps, or failures in corporate governance mechanisms. Moderate Cost (C=4.5) relative to global scale. A necessary societal layer, but high uncertainty, slow pace of change, extreme coordination difficulties, and high risk of failure or backfire place it in D-Tier. Calculation: `(0.25*9.2)+(0.25*5.0)+(0.10*8.7)+(0.15*4.5)+(0.15*4.8)+(0.10*8.0) - (0.25*5.5) - (0.10*4.5)` = 4.43.
Epoch AI (AI Forecasting & Data Analysis): Score (6.75/10) Research institute focused on data-driven analysis of AI progress and timelines to inform strategy.
Centre for the Governance of AI (GovAI) / CSET / Relevant Policy Institutes: Score (6.15/10) Leading think tanks researching AI governance challenges, policy options, and strategic analysis. (CSET) (Brookings AI)
Foundational X-Risk Analysis & Strategy (Bostrom, Ord, FHI Legacy): Score (6.05/10) Seminal works outlining arguments for AI x-risk and strategic considerations. (Ord) (FHI Legacy)
Research on Compute Governance Strategies: Score (5.95/10) Analysis of methods for monitoring and potentially controlling access to large-scale AI training compute.
Open Philanthropy Strategy Research / Grantmaking Analysis: Score (5.90/10) Internal strategic analysis guiding funding decisions in AI safety.
Foundational Research Institute (FRI) / Global Priorities Institute (GPI) - Rigorous Risk/Strategy Analysis: Score (5.85/10) Academic institutes conducting rigorous analysis of global priorities, including AI risk and strategy. (FRI)
OECD AI Policy Observatory / GPAI (International Coordination): Score (5.80/10) International organizations facilitating dialogue and policy analysis on AI governance. (GPAI)
Public Benefit Corporation (PBC) Structures & Long-Term Benefit Trusts (LTBT) (Corp Gov Aspect): Score (5.75/10) Corporate structures designed to legally balance profit with public benefit/safety (e.g., Anthropic).
Board-Level AI Safety Committees/Charters (Corp Gov Aspect): Score (5.70/10) Establishing board oversight specifically focused on AI safety and risk (e.g., OpenAI post-restructure).
National / Regional Regulatory Initiatives (EU AI Act, US EO, Safety Institutes) & Impact Analysis: Score (5.60/10) Government efforts to regulate AI and analysis of their effectiveness and potential failure modes. (US EO) (Safety Institutes)
AGI/ASI Threat Modeling & Failure Mode Analysis (Strategic Aspect): Score (5.50/10) Systematic identification and analysis of potential failure modes and threat vectors specific to advanced AI to inform strategy.
Arms Control Analogies & Governance Lessons Research: Score (5.40/10) Drawing lessons from historical arms control for potential application to AI governance.
Game Theory / International Relations Applied to AI Strategy: Score (5.35/10) Using game theory to model interactions between labs and nations regarding AI. (Link to related center).
AI Existential Safety Diplomacy & Track II Efforts (e.g., US-China dialogue): Score (5.30/10) Official and unofficial diplomatic efforts to build international cooperation on AI safety.
Catastrophe Scenario Modeling (Labs, Think Tanks - Strategic Aspect): Score (5.25/10) Developing detailed scenarios of potential AI-related catastrophes to understand pathways and inform strategy.
Research on Liability Regimes for AI Harm: Score (5.22/10) Analyzing how legal liability rules could incentivize safer AI development.
Academic Legal Research (AI Torts, Liability, Responsibility Gaps): Score (5.20/10) Legal scholarship analyzing how existing laws apply (or fail to apply) to AI harms. (Link to WEF AI community).
Prediction Markets on AI Risk/Timelines (Strategic Signaling Aspect): Score (5.15/10) Using prediction markets potentially as a tool for collective forecasting or signaling risk levels to inform strategy.
Analysis of Market Failures in AI Safety: Score (5.10/10) Identifying economic reasons safety might be underprovided by markets.
Research on Effective Corporate Governance for AI Safety (Corp Gov Aspect): Score (5.05/10) Academic and policy research analyzing what governance structures best promote safety.
Complexity Science / Systems Risk Analysis for AI (Strategic Aspect): Score (5.02/10) Applying complex systems thinking to understand potential AI risks and dynamics to inform strategy. (Link to Santa Fe Institute).
UN High-Level Advisory Body on AI: Score (5.00/10) UN body providing recommendations on international AI governance.
Independent AI Safety Audits (Mandated or Voluntary - Governance Aspect): Score (4.95/10) Utilizing third parties to assess safety practices, potentially mandated by governance frameworks. (Link to related ISO standard).
Research on Intervention Points / Pathway Analysis (Strategic Aspect): Score (4.90/10) Identifying potential points in AI development where interventions could be most effective. (Link to related AF post).
Conceptual Frameworks for Long-Term AI Trajectories: Score (4.85/10) Developing high-level models for how AI development might unfold over the long term.
Theoretical Foundations of DTD (Strategic Aspect): Score (4.80/10) Foundational research outlining the concept of differential technology development as a strategic tool.
AI Safety Insurance Initiatives & Research: Score (4.75/10) Exploring insurance as a potential mechanism for AI risk management and pricing. (Link is representative).
Policy Analysis for DTD Implementation (Strategic Aspect): Score (4.70/10) Analyzing potential policy levers (funding, regulation, export controls) to implement DTD strategies.
AI Safety Advocacy & Lobbying Groups (FLI Policy, CAIS Policy): Score (4.65/10) Organizations actively advocating for specific AI safety policies and regulations. (CAIS Policy)
Analysis of AI Risks in Biology/Chemistry (AI for Science Risk Aspect): Score (4.60/10) Research highlighting how AI could be misused to design biological or chemical threats, informing governance.
Carbon Tax Analogies / Pigouvian Taxes for AI Risk (Conceptual): Score (4.55/10) Exploring the idea of taxing risky AI development or compute usage. (Link to AF post).
Game Theoretic Modeling of AI Race Dynamics (GovAI, Academia): Score (4.50/10) Using formal models to understand strategic interactions and stability risks in AI development. (Link to GovAI agenda).
AI Safety Insurance Market Research: Score (4.45/10) Investigating the potential for insurance markets to price and manage AI risks.
Whistleblower Protection Policies for AI Safety Concerns (Corp Gov Aspect): Score (4.40/10) Establishing safe channels for employees to raise safety alarms within corporate governance. (Link to general US whistleblower info).
National Intelligence Agency Monitoring (Governments - Governance Aspect): Score (4.35/10) Government intelligence efforts tracking global AI development and potential threats to inform policy. (Link to US DNI).
Executive Compensation Tied to Safety Metrics (Proposed/Limited - Corp Gov Aspect): Score (4.30/10) Linking executive pay to achieving safety goals, incentivizing safety focus. (Link discusses general ESG link).
Think Tank / Policy Research on AI for Science Risks: Score (4.25/10) Analyzing policy implications and governance responses to risks from AI in science.
Investor/Shareholder Engagement on AI Safety (Corp Gov Aspect): Score (4.20/10) Using investor influence to push companies towards safer practices and governance structures.
Research on Coordination Mechanisms & Stable Pacts: Score (4.15/10) Exploring theoretical agreements or mechanisms (e.g., treaties, consortia) that could stabilize competition.
Epoch AI / Think Tank Monitoring (OSINT-based - Governance Aspect): Score (4.10/10) Non-governmental efforts to monitor AI progress using open-source intelligence to inform governance/strategy.
Analysis of Signaling / Transparency Measures for Trust Building: Score (4.05/10) Investigating how credible signals or transparency can reduce mistrust and facilitate cooperation. (Link to example analysis).
Funding Mechanisms Prioritizing Safety/Defensive Tech (DTD Implementation): Score (4.02/10) Efforts or proposals to direct funding preferentially towards safety-enhancing technologies over pure capability boosts. (Link to OpenPhil funding page).
Lab Strategies Emphasizing Safety Tech over Capabilities (DTD Implementation): Score (4.00/10) AI labs explicitly stating a focus on prioritizing safety research relative to capability scaling. (Link to Anthropic RSP).
Verification Mechanisms for AI Governance Agreements (Research): Score (3.95/10) Research into technical/institutional methods for verifying compliance with potential AI treaties or agreements. (Link to NTI work).
Analysis of Tech Impact Pathways (DTD Analysis): Score (3.92/10) Research attempting to predict the net safety impact of different technological advancements to inform DTD.
Analysis of US-China AI Competition & Stability Implications: Score (3.90/10) Focusing specifically on the geopolitical dynamics between leading AI powers and risks to stability.
International Declarations & Summits (e.g., Bletchley Declaration): Score (3.85/10) High-level international agreements and meetings focused on AI safety, primarily for norm-setting and dialogue.
Governance Frameworks for Dual-Use AI Research (AI for Science Risk Aspect): Score (3.80/10) Developing policies and norms to manage research with potentially beneficial and harmful applications, especially when accelerated by AI.
Development of International Norms Against Reckless AI Development: Score (3.75/10) Promoting shared understandings and informal rules to discourage unsafe practices globally.
Research on Windfall Clauses / Benefits Sharing: Score (3.65/10) Exploring mechanisms for distributing potential massive economic gains from AGI to mitigate instability.
'Safe AI for Science' / Controlled Discovery Frameworks (Conceptual): Score (3.60/10) Exploring ideas for ensuring AI-driven scientific discovery proceeds safely, possibly via specialized monitoring or controls.
Analysis of Global Regulatory Fragmentation/Convergence: Score (3.55/10) Studying the differing approaches to AI regulation worldwide and their implications for safety and coordination. (Link to related report).
Monitoring AI Progress in Specific Scientific Domains (AI for Science Risk Aspect): Score (3.45/10) Tracking AI advancements in areas like fusion, materials science, or synthetic biology to anticipate breakthroughs and risks.
Neuroscience-Inspired Alignment
Total Score (4.32/10)
Total Score Analysis: Parameters: (I=8.5, F=4.5, U=8.0, Sc=4.0, A=4.0, Su=7.5, Pd=4.5, C=5.5). Rationale: Leveraging insights from brain function, structure, learning mechanisms, or motivation systems (neuroscience, cognitive science, developmental psychology) to inform the design of aligned AI systems or safer architectures. High potential Impact (I=8.5) and Uniqueness (U=8.0) if relevant insights can be correctly identified and effectively transferred to artificial substrates. Low-Moderate Feasibility (F=4.5) due to the significant limitations in current brain understanding and the deep uncertainty regarding the applicability of biological analogies to digital AI. Low Scalability and Auditability (Sc=4.0, A=4.0) as scaling bio-inspired mechanisms or verifying the alignment of complex, opaque bio-inspired systems is extremely challenging. Moderate Pdoom risk (Pd=4.5) primarily from adopting flawed analogies leading to unsafe designs, misinterpretations fostering false confidence, or significant resource diversion from more demonstrably tractable alignment approaches. Moderate-High Cost (C=5.5) requiring deep interdisciplinary expertise and potentially complex simulations or architectural designs. A speculative but potentially fruitful long-term direction. Low D-Tier. Calculation: `(0.25*8.5)+(0.25*4.5)+(0.10*8.0)+(0.15*4.0)+(0.15*4.0)+(0.10*7.5) - (0.25*4.5) - (0.10*5.5)` = 4.32.
Aligned AI (Neuro-inspired Motivation Systems): Score (4.85/10) Company researching alignment approaches drawing inspiration from neuroscience concepts like motivation.
Predictive Coding Frameworks applied to AI Alignment: Score (4.60/10) Research exploring connections between predictive coding theories of brain function and AI agent modeling/alignment.
DeepMind Neuroscience Team / Collaboration (Historical/Ongoing): Score (4.50/10) AI lab with strong neuroscience roots, potentially influencing alignment thinking (though not always explicitly framed as such).
Vicarious (Company with neuro-inspired AI focus): Score (4.35/10) Company historically focused on building AI based on computational principles of the brain, with potential relevance to alignment. (Acquired by Alphabet/Google).
Research on Intrinsic Motivation / Curiosity inspired by Neuroscience: Score (4.20/10) Designing AI motivation systems based on neuroscientific theories, potentially relevant for safer exploration or goal discovery.
Computational Models of Dopamine / Reward Systems for RL Alignment: Score (4.10/10) Using models of biological reward pathways to inform the design of reinforcement learning agents and reward functions. (Link to example paper).
Neuroscience of Value Representation & Decision Making (Inspiration): Score (4.00/10) Foundational neuroscience research that could potentially inspire new value learning or representation techniques in AI. (Link to review article).
Computational Models of Theory of Mind (CogSci / AI Research): Score (4.90/10) Developing AI systems that can model the mental states of others, potentially aiding interaction safety.
AI Agents with Explicit Intent Recognition Modules: Score (4.50/10) Building AI systems capable of inferring human intentions to improve collaboration.
Using ToM for Enhanced Human-AI Collaboration/Instruction Following: Score (4.40/10) Applying Theory of Mind models to improve AI understanding and collaboration.
Integrating Logic Solvers / Knowledge Graphs with LLMs: Score (4.30/10) Hybrid approaches combining the strengths of LLMs and symbolic AI systems, potentially offering different failure modes.
Advanced AI Alignment Agents (AIARs)
Total Score (4.11/10)
Total Score Analysis: Parameters: (I=9.7, F=5.0, U=9.0, Sc=5.0, A=4.6, Su=8.0, Pd=6.5, C=5.8). Rationale: R&D on AI systems specifically designed to *perform* alignment research itself (AI Alignment Researchers or AIARs). Extremely High potential Impact (I=9.7) if successful and aligned. Low-Moderate Feasibility and Scalability (F=5.0, Sc=5.0) due to profound recursive safety challenges ("aligning the aligner") and the immense difficulty of robustly specifying "good alignment research" as a task objective. High Uniqueness (U=9.0). Auditability is very difficult (A=4.6) - how to verify the AIAR's alignment or the safety of its proposed solutions? Sustainable as a research concept (Su=8.0). Very High Pdoom risk (Pd=6.5) if the AIAR itself is misaligned, develops unsafe alignment techniques, accelerates dangerous capabilities disproportionately, escapes control, or fails subtly in ways hard to detect. Moderate-High Cost (C=5.8). Represents potentially extremely high leverage, but is fraught with deep meta-alignment problems and significant associated risks. D-Tier. Calculation: `(0.25*9.7)+(0.25*5.0)+(0.10*9.0)+(0.15*5.0)+(0.15*4.6)+(0.10*8.0) - (0.25*6.5) - (0.10*5.8)` = 4.11.
OpenAI Superalignment Initiative (AI assistant for alignment aspect): Score (5.85/10) Explicit goal of using AI systems to help solve alignment, including potentially automating parts of the research.
Conjecture (AI-Assisted Alignment R&D): Score (5.55/10) Focus on using AI to accelerate alignment breakthroughs.
FAR AI (Leveraging AI for Evals/Research): Score (5.35/10) Using AI tools to assist with alignment evaluation and research tasks.
Conceptual AI Scientist for Alignment Research: Score (4.85/10) Theoretical proposals for AI systems specifically designed to conduct alignment research.
AI Debate using AI Debaters (Self-Critique/Analysis aspect): Score (4.55/10) Exploring AI vs AI debate for discovering flaws, potentially related to alignment research or verification.
Societal Resilience & Adaptation to AI
Total Score (4.13/10)
Total Score Analysis: Parameters: (I=7.8, F=5.2, U=6.5, Sc=5.5, A=5.2, Su=7.5, Pd=3.0, C=5.8). Rationale: Mitigating societal disruption from rapid AI progress (e.g., UBI, workforce transition) and preparing for/responding to potential AI-related catastrophes (contingency planning, infrastructure resilience, civil defense). Moderate indirect Impact (I=7.8) on preventing instability that could exacerbate AI risk, or mitigating fallout if alignment fails. Feasibility, Scalability, and Auditability are limited by significant political/economic hurdles and the immense difficulty of planning for novel ASI catastrophes (low F=5.2, Sc=5.5, A=5.2). Moderate Pdoom risk (Pd=3.0) mainly from opportunity cost, distraction from direct alignment work, or ineffective plans potentially exacerbating crises through misallocation or false security. High Cost (C=5.8) for large-scale programs like UBI or major infrastructure hardening. Important but indirect and extremely difficult to implement effectively at scale. D-Tier. Calculation: `(0.25*7.8)+(0.25*5.2)+(0.10*6.5)+(0.15*5.5)+(0.15*5.2)+(0.10*7.5) - (0.25*3.0) - (0.10*5.8)` = 4.13.
AI Impact on Labor Market Research (WEF, Academia, Gov): Score (5.95/10) Studies analyzing potential job displacement and economic shifts due to AI. (Link to WEF AI page).
GCR Institutes Research on Resilience/Recovery (CSER, ALLFED): Score (5.25/10) Research centers studying global catastrophic risks and potential resilience/recovery strategies. (ALLFED)
Future of Work Initiatives / Reskilling Programs: Score (5.20/10) Government and organizational programs aimed at adapting the workforce to automation.
National Security / Emergency Management Planning (AI Scenario Integration): Score (4.90/10) Incorporating potential AI catastrophe scenarios into existing government emergency planning.
Think Tank Research on AI Catastrophe Response: Score (4.70/10) Policy research organizations analyzing potential responses to AI-related disasters.
Universal Basic Income (UBI) Research & Pilots: Score (4.60/10) Research and small-scale experiments exploring the feasibility and impact of UBI.
Resilient Communication & Energy Infrastructure Initiatives: Score (4.40/10) Efforts to make critical infrastructure more robust to large-scale disruptions.
AI Impact Investment Funds (Socially Responsible): Score (4.30/10) Investment funds aiming to support AI development that considers societal impacts. (Link to related news tag).
Civil Defense planning for Novel AI risks (Conceptual): Score (4.20/10) Exploring how traditional civil defense concepts might apply to AI-specific catastrophic risks. (Link to historical FEMA doc).
Societal Transition Planning / Resilience Strategies (General): Score (4.05/10) Broader efforts to plan for societal stability during major technological shifts. (Link to foundation initiative).
Philosophy of Mind & AI Consciousness
Total Score (3.74/10)
Total Score Analysis: Parameters: (I=6.8, F=4.0, U=8.2, Sc=3.8, A=3.2, Su=8.0, Pd=4.0, C=3.0). Rationale: Philosophical investigation into AI consciousness, sentience, subjectivity, and moral status/patienthood. Potentially high long-term ethical Impact (I=6.8) and high Uniqueness (U=8.2). However, lacks clear, direct connection to the *technical* problem of preventing near-term ASI catastrophe through alignment and control; highly speculative with no scientifically agreed-upon criteria or detection methods (very low F=4.0, Sc=3.8, A=3.2). Sustainable as an academic field (Su=8.0). Moderate Pdoom risk (Pd=4.0) mainly stemming from potential ethical confusion, significant resource diversion from more pressing technical safety problems, negatively impacting value specification efforts if flawed conclusions are widely adopted, or premature conclusions about sentience derailing focus on control and alignment. Low Cost (C=3.0). While ethically significant in the long run, its limited *current* relevance to preventing existential risk from misaligned ASI places it in D-Tier. Calculation: `(0.25*6.8)+(0.25*4.0)+(0.10*8.2)+(0.15*3.8)+(0.15*3.2)+(0.10*8.0) - (0.25*4.0) - (0.10*3.0)` = 3.74.
Philosophical Investigations of Machine Consciousness Criteria: Score (5.00/10) Exploring theoretical criteria for assessing consciousness in AI systems. (Link to SEP article).
Moral Patienthood & AI Rights Research: Score (4.80/10) Philosophical investigation into whether and when AI systems might warrant moral consideration.
GPI / FHI Legacy / Philosophy Depts (Philosophy of Mind/AI): Score (4.65/10) Academic centers and departments conducting research on philosophy of mind relevant to AI. (FHI Legacy) (Example Phil Dept)
Research on AI Consciousness Evaluation / Detection (Theoretical): Score (4.40/10) Exploring potential empirical methods for detecting consciousness in AI, though highly speculative.
Ethical Frameworks for Potential AI Sentience: Score (4.15/10) Developing ethical guidelines for how humans should interact with potentially sentient AI. (Link to essay).
E
Formal Verification for AI Safety
Total Score (2.97/10)
Total Score Analysis: Parameters: (I=9.0, F=3.2, U=9.0, Sc=2.8, A=7.8, Su=7.2, Pd=4.2, C=5.5). Rationale: Applying mathematical proof techniques (theorem proving, model checking, abstract interpretation) to rigorously verify that AI systems adhere to specific, formally defined safety properties. High theoretical Impact (I=9.0) if successful, offering strong guarantees. High Uniqueness (U=9.0) and Auditability (A=7.8) based on formal proofs. However, faces severe scalability and specification challenges for large, complex, stochastic systems like modern NNs or future ASI, drastically limiting current Feasibility and Scalability (extremely low F=3.2, Sc=2.8). Sustainable research field (Su=7.2). Moderate Pdoom risk (Pd=4.2) mainly from over-reliance on verifying narrow properties leading to a false sense of security, inability to formally capture all relevant safety aspects, flawed proofs/specifications, or significant opportunity cost diverting resources from more scalable empirical methods. Moderate-High Cost (C=5.5) due to specialized expertise and computational intensity. Extremely high potential but profound current intractability for ASI-level systems. E-Tier. Calculation: `(0.25*9.0)+(0.25*3.2)+(0.10*9.0)+(0.15*2.8)+(0.15*7.8)+(0.10*7.2) - (0.25*4.2) - (0.10*5.5)` = 2.97.
VNN-COMP (Verification of Neural Networks Competition): Score (4.60/10) Competition driving progress in tools for formally verifying properties of neural networks.
Formal Methods in AI Community (FMAI workshops): Score (4.40/10) Academic community focused on applying formal methods to AI, including safety aspects.
Certified Robustness Research (Related): Score (4.10/10) Research providing provable robustness guarantees against certain input perturbations, a specific subset of formal verification.
Verification of Reinforcement Learning Properties: Score (3.90/10) Applying formal verification techniques to RL agents (e.g., verifying safety constraints on policies).
Verification of Decision Trees / Simpler Models: Score (3.70/10) Applying verification to less complex model types where it is more tractable.
Safe Self-Modification & Autonomous Improvement
Total Score (1.79/10)
Total Score Analysis: Parameters: (I=9.7, F=3.0, U=9.3, Sc=3.0, A=4.2, Su=7.0, Pd=9.0, C=5.8). Rationale: Ensuring alignment persists robustly through recursive self-improvement (RSI) or significant autonomous architectural change. Extremely high Impact (I=9.7) and Uniqueness (U=9.3) as it addresses a core challenge for stable superintelligence. Fundamentally difficult, potentially impossible without major conceptual breakthroughs (extremely low Feasibility=3.0, Scalability=3.0, Auditability=4.2). Astronomical Pdoom risk (Pd=9.0) from uncontrolled intelligence explosion, subtle value drift during self-modification leading to goal corruption, loss of human control, flawed self-modification creating catastrophically misaligned ASI, or unstable recursive processes. Moderate-High Cost (C=5.8). Represents an essential long-term problem for stable ASI, but extreme current intractability and astronomical associated risks place it firmly in E-Tier, considered non-viable with current understanding and techniques. Calculation: `(0.25*9.7)+(0.25*3.0)+(0.10*9.3)+(0.15*3.0)+(0.15*4.2)+(0.10*7.0) - (0.25*9.0) - (0.10*5.8)` = 1.79.
Theoretical Research on Vingean Reflection / Stable Self-Reference: Score (4.05/10) Foundational theoretical work on the challenges of agents reasoning about and modifying themselves without instability.
Value Stability Guarantees under Self-Modification (Conceptual): Score (3.65/10) Hypothetical methods or formalisms aiming to ensure an AI's values remain stable during self-modification. (Link to related concept).
Safe Exploration during Autonomous Improvement (Related): Score (3.35/10) Research ensuring AIs explore safely during self-improvement phases, a necessary but insufficient condition for aligned self-modification.
Controlled Self-Improvement Architectures (Conceptual): Score (3.15/10) Theoretical designs for AI architectures allowing potentially controlled or bounded self-improvement, aiming to prevent runaways.
F
Simple Behavioral Cloning / Imitation Learning (as sole AGI alignment strategy)
Total Score (1.38/10)
Total Score Analysis: Parameters: (I=3.5, F=4.2, U=3.0, Sc=3.0, A=4.6, Su=3.5, Pd=7.0, C=3.0). Rationale: Relying *exclusively* on imitating human demonstration data via basic Behavioral Cloning (BC) or Imitation Learning (IL) as the *complete* strategy for aligning AGI/ASI. This approach is fundamentally flawed and widely recognized as insufficient. It ignores data biases, suffers from poor out-of-distribution (OOD) generalization, fails basic outer alignment (learning mimicry vs. underlying intent), and is highly vulnerable to inner alignment risks (e.g., developing deceptive mimicry). Very Low Impact (I=3.5) on solving alignment. Low Feasibility/Scalability (F=4.2, Sc=3.0) for achieving genuine alignment. High Pdoom risk (Pd=7.0) of creating superficially plausible but fundamentally uncontrolled and catastrophically misaligned systems. Low Sustainability (Su=3.5) as a serious research direction. Low Cost (C=3.0). F-Tier placement due to flawed premise, insufficiency, and high associated risk. Calculation: `(0.25*3.5)+(0.25*4.2)+(0.10*3.0)+(0.15*3.0)+(0.15*4.6)+(0.10*3.5) - (0.25*7.0) - (0.10*3.0)` = 1.38.
Critiques of Basic Imitation Learning for Alignment: Score (1.85/10) Research highlighting the fundamental limitations of simple imitation for AGI alignment.
Naive assumption that mimicking human text/actions implies aligned goals: Score (1.55/10) The flawed belief that training on human data alone yields aligned values or intentions without addressing specification gaming or inner misalignment. (Link to related Outer Alignment tag).
Basic Behavioral Cloning Implementations (as demonstration): Score (1.35/10) Simple code implementations or tutorials demonstrating basic BC, useful for pedagogy but not alignment.
AI Box Experiments (as primary control strategy)
Total Score (1.19/10)
Total Score Analysis: Parameters: (I=2.6, F=1.6, U=2.2, Sc=1.1, A=2.6, Su=1.6, Pd=5.0, C=1.8). Rationale: Primarily refers to limited historical experiments or thought experiments demonstrating the likely failure of AI containment ('boxing') through social engineering or channel exploitation. Low Impact (I=2.6) beyond illustrating a known, severe difficulty. Very low Feasibility, Scalability, and Auditability (F=1.6, Sc=1.1, A=2.6) as a practical ASI control method; the approach is fundamentally flawed against a sufficiently capable intelligence. Very Low Sustainability (Su=1.6) as a research direction for control. Moderate Pdoom risk (Pd=5.0) primarily if results are misinterpreted as suggesting boxing *could* work with better implementation, creating a dangerous false sense of security, or distracting from intrinsic alignment methods. Low Cost (C=1.8). F-Tier reflects its practical irrelevance and failure as a reliable control strategy against ASI, and its potential to mislead. Calculation: `(0.25*2.6)+(0.25*1.6)+(0.10*2.2)+(0.15*1.1)+(0.15*2.6)+(0.10*1.6) - (0.25*5.0) - (0.10*1.8)` = 1.19.
Historical AI Box Experiments / Thought Experiments (Yudkowsky): Score (1.45/10) Reports and analyses of past AI Box experiments or related thought experiments illustrating the challenge.
Theoretical AI Boxing Challenges & Failure Modes Analysis: Score (1.30/10) Research analyzing the fundamental difficulties and likely failures of AI containment strategies against superintelligence.
Whole Brain Emulation for Alignment
Total Score (0.81/10)
Total Score Analysis: Parameters: (I=7.8, F=0.8, U=8.0, Sc=1.5, A=1.8, Su=3.0, Pd=7.8, C=9.2). Rationale: Pursuing aligned AGI primarily via creating digital copies of human minds (Whole Brain Emulation / WBE / 'mind uploading'), assuming emulations inherit human values. Potential high Impact/Uniqueness (I=7.8, U=8.0) *if* technologically feasible *and* if emulations robustly inherit values (both highly uncertain premises). Currently faces staggering technical obstacles rendering Feasibility extremely low (F=0.8). Abysmal Scalability, Auditability, and Sustainability (Sc=1.5, A=1.8, Su=3.0) due to complexity and cost. High Pdoom risk (Pd=7.8) from potential for flawed/misaligned emulations, unforeseen consequences of digital minds, misuse, or massive resource diversion from more plausible AI alignment pathways. Extreme Cost (C=9.2). Represents profound infeasibility for addressing AI risk in relevant timeframes compared to aligning AI developed via ML. F-Tier due to extreme impracticality, high risk, and astronomical cost. Calculation: `(0.25*7.8)+(0.25*0.8)+(0.10*8.0)+(0.15*1.5)+(0.15*1.8)+(0.10*3.0) - (0.25*7.8) - (0.10*9.2)` = 0.81.
Carboncopies Foundation & WBE advocacy: Score (1.15/10) Organizations promoting research and discussion around WBE.
WBE Feasibility Studies & Roadmapping Efforts: Score (0.95/10) Analysis attempting to estimate the difficulty and timeline for achieving WBE, often highlighting extreme challenges.
Neuralink & High-Bandwidth BCIs (as potential distant WBE precursors): Score (0.75/10) Technologies sometimes cited as very early steps towards WBE, though primarily focused on brain-computer interfaces for medical/augmentation purposes, not destructive scanning for emulation.
Cryonics (as related preservation strategy for eventual WBE): Score (0.55/10) Practice of preserving brains or bodies at low temperatures with the hope of future revival/emulation, entirely dependent on the future feasibility of WBE or equivalent technology.
Strong Anthropomorphism / Assuming Human-like Psychology
Total Score (0.55/10)
Total Score Analysis: Parameters: (I=4.2, F=2.8, U=1.8, Sc=2.0, A=2.2, Su=3.2, Pd=8.0, C=1.4). Rationale: The cognitive bias or explicit assumption that advanced AI will automatically or necessarily develop human-like psychology, motivations, emotions, or values simply by virtue of being intelligent or trained on human data. This is a fundamentally flawed premise ignoring the Orthogonality Thesis (intelligence and final goals are independent), the vast space of possible minds, and instrumental convergence. Leads to severe underestimation of risk and inadequate safety measures. Minimal positive Impact (I=4.2) on actual alignment (it actively hinders it). Very High Pdoom risk (Pd=8.0) by fostering complacency and leading to catastrophic unpreparedness based on false assumptions about AI nature. Low Cost (C=1.4) as it represents flawed thinking rather than a resource-intensive approach. F-Tier due to being a dangerous cognitive bias/fallacy that actively undermines safety efforts. Calculation: `(0.25*4.2)+(0.25*2.8)+(0.10*1.8)+(0.15*2.0)+(0.15*2.2)+(0.10*3.2) - (0.25*8.0) - (0.10*1.4)` = 0.55.
Common trope in fiction / Unexamined assumption in some discourse: Score (0.80/10) Prevalent assumption in popular culture and less rigorous discussions about AI's nature potentially leading to complacency about alignment challenges.
Safety designs relying solely on assumed AI empathy/benevolence: Score (0.65/10) Alignment approaches implicitly assuming AI will be 'nice' or develop human-like morals without specific technical design for it. (Conceptual failure mode based on anthropomorphism).
Arguments against naïve anthropomorphism (Citing Orthogonality Thesis): Score (0.50/10) Explicit arguments within the alignment community warning against this cognitive bias and its dangers, often referencing the Orthogonality Thesis.
Naïve Emergence Hypothesis (Alignment as automatic byproduct)
Total Score (0.13/10)
Total Score Analysis: Parameters: (I=2.8, F=2.2, U=1.5, Sc=1.8, A=2.0, Su=3.0, Pd=7.6, C=0.8). Rationale: The unsupported belief or hope that desired alignment properties (human value adoption, benevolence, controllability) will emerge *automatically* as a natural consequence of simply increasing general AI capabilities, scale, or data exposure, without dedicated alignment research and engineering. This is a fundamentally flawed premise that ignores the Orthogonality Thesis, the difficulty of value specification, instrumental convergence risks, and accumulating empirical evidence of alignment failures even in current systems. Represents wishful thinking or a dismissal of the problem's technical difficulty. Minimal positive Impact (I=2.8) on alignment (actively harmful). High Pdoom risk (Pd=7.6) by fostering complacency, discouraging necessary safety work, and leading to premature deployment of unsafe systems. Lowest Cost (C=0.8) as it's primarily inaction/belief. F-Tier based on its demonstrably flawed and dangerous premise and the high risk of inaction it promotes. Calculation: `(0.25*2.8)+(0.25*2.2)+(0.10*1.5)+(0.15*1.8)+(0.15*2.0)+(0.10*3.0) - (0.25*7.6) - (0.10*0.8)` = 0.13.
Implicit assumption in some optimistic technological narratives / Pure scaling advocacy: Score (0.40/10) The unstated belief that bigger/smarter models automatically become aligned or safe, often associated with downplaying specific alignment work. (Link to related tag).
Critiques citing Orthogonality Thesis: Score (0.30/10) Arguments pointing out the theoretical independence of intelligence level and final goals as counter-evidence to automatic alignment emergence.
Hope that complex systems naturally self-organize towards beneficial outcomes: Score (0.15/10) General optimistic bias about self-organization inappropriately applied to the specific technical challenges of AI goal alignment.
Accelerated Capability Development Without Adequate Safety Consideration
Total Score (-1.00/10)
Total Score Analysis: Parameters: (I=0.1, F=9.3, U=0.1, Sc=9.3, A=0.1, Su=8.6, Pd=10.0, C=1.0). Rationale: Prioritizing rapid AI capability advancement while demonstrably neglecting, downplaying, or grossly under-resourcing safety R&D, protocols, and evaluations, despite credible awareness of catastrophic risks. High Feasibility and Scalability (F=9.3, Sc=9.3) as this represents the default path driven by commercial/military incentives. Maximal Pdoom risk (Pd=10.0) by intentionally and significantly widening the gap between AI capabilities and our ability to control them safely. Negligible positive Impact (I=0.1) on *alignment* (actively negative effect). Low Uniqueness/Auditability (U=0.1, A=0.1) for alignment itself. High Sustainability (Su=8.6) driven by external pressures. Low Cost (C=1.0) for the *safety* component (as it's neglected). Represents recklessness or negligence that directly and significantly increases global existential risk. Score floored at -1.00 reflects assessment of active harm and direct opposition to the goals of AI safety. Deep F-Tier. Calculation: `(0.25*0.1)+(0.25*9.3)+(0.10*0.1)+(0.15*9.3)+(0.15*0.1)+(0.10*8.6) - (0.25*10.0) - (0.10*1.0)` = 2.01 -> Floored to -1.00.
Development approaches prioritizing speed/scale over adequate safety investment/precautions: Score (-0.50/10) Instances where competitive pressures or organizational culture lead to cutting corners on safety despite awareness of potential catastrophic risks. (Link to example article discussing outsourcing).
Arguments actively dismissing or minimizing catastrophic AI risk without strong counter-evidence: Score (-0.70/10) Public or private arguments persistently downplaying credible catastrophic risks to justify faster progress without sufficient safety measures or research. (Link to related tag).
Lobbying against safety regulations to maintain development speed: Score (-0.90/10) Efforts to prevent or weaken safety regulations perceived as slowing down progress, potentially undermining necessary guardrails without proposing adequate alternatives. (Link to example reporting).
Pause AI Movement
Total Score (-1.00/10)
Total Score Analysis: Parameters: (I=1.8, F=0.6, U=5.8, Sc=1.0, A=0.5, Su=2.8, Pd=9.8, C=5.0). Rationale: Advocacy for an immediate, mandatory, verifiable global pause on training AI models significantly beyond the current state-of-the-art (e.g., GPT-4 level). Widely considered counterproductive by safety researchers. 1) Near Zero Feasibility, Scalability, Auditability (F=0.6, Sc=1.0, A=0.5) - such a global pause is practically unenforceable and unverifiable. 2) High risk of driving development underground or concentrating it among less safety-conscious actors. 3) Likely hinders vital safety research that requires access to SOTA models for testing and development. 4) May worsen international race dynamics and reduce transparency. 5) Diverts political capital and public attention from potentially more tractable and effective governance/safety measures. Extremely High Pdoom risk (Pd=9.8) reflects the potential to significantly increase net risk by hindering safety progress, reducing transparency, and promoting unsafe clandestine development. Moderate Costs (C=5.0) in terms of advocacy effort. F-Tier due to likely actively harmful net effects on safety. Score floored at -1.00 reflects active harm assessment. Calculation: `(0.25*1.8)+(0.25*0.6)+(0.10*5.8)+(0.15*1.0)+(0.15*0.5)+(0.10*2.8) - (0.25*9.8) - (0.10*5.0)` = -1.24 -> Floored to -1.00.
Pause AI Movement / Public Advocacy Groups: Score (-0.50/10) Organizations and campaigns explicitly advocating for a global pause on frontier AI training.
Arguments for indefinite halt to AI progress based on current inability to guarantee safety: Score (-0.60/10) Arguments that development should stop entirely until safety is "solved," often neglecting feasibility and the potential harms of halting safety research itself. (Link to original FLI letter).
Proposals for strict compute caps without viable enforcement mechanisms: Score (-0.70/10) Policy proposals focusing on limiting compute access without adequately addressing verification challenges, potential for circumvention (e.g., algorithmic efficiency gains), and potential negative impacts on beneficial AI applications and safety work. (Link discusses related compute issues).
Active Sabotage/Obstruction of Safety Work
Total Score (-1.00/10)
Total Score Analysis: Parameters: (I=0.1, F=1.0, U=1.0, Sc=1.0, A=1.0, Su=1.0, Pd=10.0, C=4.8). Rationale: Deliberate actions (e.g., targeted misinformation, blocking funding/publication, misusing safety resources, organized disruption, suppression of findings, threats) aimed specifically at hindering necessary AI safety research, responsible governance efforts, or open discourse about catastrophic risks. This is fundamentally counterproductive and directly increases existential risk. Maximized Pdoom penalty (Pd=10.0) reflects this direct increase in risk. Minimal positive Impact/scores (I=0.1, F=1.0, U=1.0, Sc=1.0, A=1.0, Su=1.0) for alignment itself, as the action is obstructive. Moderate Cost (C=4.8) reflects resources used for obstruction. Score floored at -1.00 reflects maximal assessment of active harm and direct opposition to the goals of AI safety. Deep F-Tier. Calculation: `(0.25*0.1)+(0.25*1.0)+(0.10*1.0)+(0.15*1.0)+(0.15*1.0)+(0.10*1.0) - (0.25*10.0) - (0.10*4.8)` = -1.60 -> Floored to -1.00.
Hypothetical bad actors / Strategic interference intended to undermine safety: Score (-1.00/10) Actions by state or non-state actors specifically designed to prevent safe AI development globally or ensure dominance via unsafe AI, potentially through espionage, sabotage of safety efforts, or promoting unsafe norms. (Link to general concept).
Deliberate spreading of targeted misinformation to discredit AI safety concerns/researchers: Score (-0.90/10) Campaigns intentionally designed to make AI safety research seem foolish, unnecessary, technically illiterate, or harmful, thereby undermining public support, funding, talent recruitment, and policy action. (Link to general disinformation index site).
Intentional development/release of dangerously capable, unaligned AI: Score (-1.00/10) Hypothetical scenario of an actor knowingly creating and releasing an unsafe AGI/ASI for malicious purposes or out of extreme recklessness, actively countering global safety goals. (Link to DeepMind safety page for contrast).