Qwen3-235B-A22B
S
A
Comprehensive AI Safety Education
Total Score (8.35/10)
Total Score Analysis: Parameters: (I=9.5, F=9.0, U=6.0, Sc=9.5, A=7.5, Su=9.0, Pd=0.5, C=2.0). Rationale: Essential force multiplier increasing talent, research quality, and coordination capacity. High Impact/Feasibility/Scalability/Sustainability. Excellent foundational support. Auditability through program outcomes moderate. Minimal direct risk (Pd=0.5), low relative Cost. Crucial support infrastructure enabling the field's growth and effectiveness globally. Remains firmly A-Tier. Calculation: `(0.25*9.5)+(0.25*9.0)+(0.10*6.0)+(0.15*9.5)+(0.15*7.5)+(0.10*9.0) - (0.25*0.5) - (0.10*2.0)` = 8.35.
Description: Systematic development and dissemination of AI safety, alignment, and ethics knowledge to researchers, engineers, policymakers, students, and the public to foster a well-informed global community capable of tackling alignment challenges. Includes online forums, courses, career advising, training programs, and mentorship.
Alignment Forum: Score (8.70/10)
Central online hub for technical discussions, research, debates, and community building.
aiSafety.info (Rob Miles): Score (8.20/10)
Effective public communication simplifying complex concepts for broad understanding.
BlueDot Impact (incl. former AISF): Score (8.00/10)
Structured educational programs and fellowships for onboarding talent into the field.
80,000 Hours (AI Safety Career Advice): Score (7.92/10)
Guides individuals towards impactful AI safety career paths, influencing talent allocation.
Mechanistic Interpretability
Total Score (7.55/10)
Total Score Analysis: Parameters: (I=9.9, F=7.5, U=9.2, Sc=7.8, A=9.0, Su=9.0, Pd=2.2, C=7.5). Rationale: Aims to reverse-engineer neural network computations, crucial for verifying alignment and detecting hidden failures like deception. Extremely high Impact/Uniqueness/Auditability potential. Feasibility/Scalability rapidly improving with techniques like SAEs, but applying reliably to frontier models remains challenging (moderate F/Sc). Very high Cost (talent/compute). Moderate Pdoom risk (2.2) from potential infohazards or enabling misuse. Core research direction justifiably enters A-Tier due to foundational importance and recent progress. Calculation: `(0.25*9.9)+(0.25*7.5)+(0.10*9.2)+(0.15*7.8)+(0.15*9.0)+(0.10*9.0) - (0.25*2.2) - (0.10*7.5)` = 7.55.
Description: The pursuit of understanding the internal workings, representations, computations, and causal mechanisms within AI models (especially neural networks) at the level of individual components and circuits to predict behavior, identify safety-relevant properties, enable targeted interventions, and verify alignment claims. Focuses on 'reverse engineering' the model.
Anthropic Mechanistic Interpretability Team: Score (8.07/10)
Leading research on transformer circuits, superposition, SAEs, scalable interpretability.
Neel Nanda / Transformer Circuits Community: Score (7.72/10)
Influential researcher, community hub, tool development (TransformerLens).
OpenAI Interpretability Research: Score (7.67/10)
Focus on understanding representations, concept mapping, SAEs, Superalignment link.
Google DeepMind Interpretability Teams: Score (7.42/10)
Research on feature viz, causal analysis, representation analysis in large models.
Cooperative Inverse Reinforcement Learning (CIRL)
Total Score (7.73/10)
Total Score Analysis: Parameters: (I=9.5, F=8.2, U=9.0, Sc=7.0, A=8.5, Su=8.0, Pd=1.5, C=6.0). Rationale: Provides formal framework for human-AI value alignment where AI actively infers and optimizes for human values while acknowledging uncertainty. Strong theoretical foundation (Stuart Russell's work). High impact on shaping alignment discourse. Good auditability through mathematical formalism. Moderate scalability challenges. Low Pdoom risk. Calculation: `(0.25*9.5)+(0.25*8.2)+(0.10*9.0)+(0.15*7.0)+(0.15*8.5)+(0.10*8.0) - (0.25*1.5) - (0.10*6.0)` = 7.73.
Description: A formal framework for value alignment where an AI agent actively infers and acts according to its user's preferences while explicitly modeling uncertainty about those preferences. Positions AI as cooperative partner rather than passive executor. Builds rigorous mathematical foundation for preference inference and assistance games.
Original CIRL Paper (Hadfield-Menell et al. 2016): Score (8.30/10)
Pioneering work establishing the Cooperative Inverse Reinforcement Learning framework.
CIRL & Value Learning Research (ARC/MIRI): Score (8.10/10)
Extending CIRL principles to more complex scenarios and multi-agent settings.
Berkeley Center for Human-Compatible AI: Score (7.85/10)
Continuing research implementing CIRL principles in practical AI systems.
B
AI-Assisted Alignment Research
Total Score (7.30/10)
Total Score Analysis: Parameters: (I=9.9, F=9.0, U=8.8, Sc=9.5, A=7.8, Su=9.2, Pd=4.0, C=6.5). Rationale: Central strategy leveraging AI to accelerate alignment R&D. Immense Impact/Scalability potential. High Feasibility/Sustainability using current systems. Moderate Auditability, proving oversight effectiveness complex. Significant Pdoom risk (4.0) from "aligning the aligner," misuse, or masking deeper issues. High Cost (compute, expertise). Key strategic lever, but requires vigilant risk management. High B-Tier position reflecting potential balanced by risks/costs. Calculation: `(0.25*9.9)+(0.25*9.0)+(0.10*8.8)+(0.15*9.5)+(0.15*7.8)+(0.10*9.2) - (0.25*4.0) - (0.10*6.5)` = 7.30.
Description: Employing AI systems as tools to augment human capabilities in understanding AI internals, evaluating alignment properties, generating alignment solutions, discovering flaws, or performing oversight tasks, aiming to scale alignment research alongside or ahead of AI capabilities. Focuses on using AI as a tool for alignment R&D itself.
OpenAI Superalignment Initiative: Score (7.90/10)
Major initiative explicitly using current models to research/evaluate alignment for future superintelligence.
Anthropic AI-Assisted Research Scaling: Score (7.70/10)
Using models for evaluation, critique, interpretability tasks, key to scaling/oversight.
DeepMind's Recursive Reward Modeling & Debate: Score (7.20/10)
AI assists human oversight by refining objectives (RRM) or evaluating arguments (Debate). Early examples.
Redwood Research Automated Interpretability/Adversarial Training: Score (6.90/10)
Using AI as adversaries/assistants to find vulnerabilities or salient features automatically.
Recursive Reward Modeling
Total Score (7.15/10)
Total Score Analysis: Parameters: (I=9.2, F=8.0, U=8.5, Sc=7.5, A=8.2, Su=7.8, Pd=3.5, C=6.8). Rationale: Promising approach decomposing complex tasks into simpler subtasks that can be evaluated more reliably. Builds hierarchical reward functions through recursive application of reward learning. High impact on shaping successor approaches. Moderate feasibility demonstrated in limited domains. Good auditability through decomposition transparency. Moderate scalability limitations. Calculation: `(0.25*9.2)+(0.25*8.0)+(0.10*8.5)+(0.15*7.5)+(0.15*8.2)+(0.10*7.8) - (0.25*3.5) - (0.10*6.8)` = 7.15.
Description: Approach to value alignment where a base reward function is learned from human feedback, then used to train agents that assist humans in providing higher-quality feedback for increasingly complex tasks. Creates a recursive loop where trained agents help evaluate and refine their successors, potentially scaling alignment with capability increases.
DeepMind's Original Recursive Reward Modeling Work: Score (7.60/10)
Pioneering implementation exploring recursive application of reward learning frameworks.
Foundational RRMs Papers: Score (7.35/10)
Theoretical groundwork establishing recursive reward modeling as alignment approach.
Agent Foundry Implementations: Score (7.00/10)
Practical implementations testing RRMs in real-world environments and applications.
Eliciting Latent Knowledge (ELK)
Total Score (7.05/10)
Total Score Analysis: Parameters: (I=9.5, F=7.2, U=9.0, Sc=6.8, A=8.5, Su=7.3, Pd=2.8, C=6.5). Rationale: Critical research on incentivizing AI systems to truthfully reveal what they know rather than optimizing for human approval. High impact on understanding deceptive alignment. Moderate feasibility shown in toy problems. Good auditability through explicit problem formulation. Scalability challenges remain significant. Calculation: `(0.25*9.5)+(0.25*7.2)+(0.10*9.0)+(0.15*6.8)+(0.15*8.5)+(0.10*7.3) - (0.25*2.8) - (0.10*6.5)` = 7.05.
Description: Research focused on developing methods to encourage AI systems to reveal their true knowledge and beliefs rather than simply telling humans what they want to hear. Addresses core challenge of verifying AI honesty and avoiding deceptive alignment. Seeks to distinguish between "knowing" and "telling" in AI systems.
ARC's ELK Problem Statement & Research: Score (7.60/10)
Defining work introducing and framing the Eliciting Latent Knowledge problem space.
ELK Technical Reports & Benchmarks: Score (7.25/10)
Technical exploration of ELK solutions and evaluation frameworks.
ELK Community Research & Commentary: Score (6.90/10)
Community-driven exploration and expansion of ELK concepts across diverse contexts.
C
Catastrophic Risk Scenario Modeling & Analysis
Total Score (6.19/10)
Total Score Analysis: Parameters: (I=9.0, F=7.0, U=7.8, Sc=6.2, A=7.0, Su=7.8, Pd=1.7, C=3.8). Rationale: Construction/analysis of detailed plausible AI catastrophe pathways. High Impact grounding abstract risks, informing threat models/red teaming. Moderate Feasibility (realistic scenarios hard to generate). Moderate Auditability (scenario coherence). Moderate Pdoom risk (1.7) from infohazards. Bridges general X-Risk analysis with specific evaluation design. High C-Tier essential work for concretizing risks. Calculation: `(0.25*9.0)+(0.25*7.0)+(0.10*7.8)+(0.15*6.2)+(0.15*7.0)+(0.10*7.8) - (0.25*1.7) - (0.10*3.8)` = 6.19.
Description: Research focused on constructing and analyzing detailed, plausible scenarios describing pathways to AI-related catastrophes. Aims to move beyond abstract risk categories to specific failure modes, system dynamics, contributing factors, and potential consequences, thereby informing threat models, guiding capability evaluations and red teaming efforts, identifying critical vulnerabilities, and supporting strategic prioritization and preparedness planning.
Lab Internal Scenario Development Teams (Confidential): Score (6.59/10)
Internal efforts mapping potential catastrophic failure pathways to guide internal safety/eval priorities.
Think Tank Scenario Reports (RAND, CSET, GovAI, FHI Legacy): Score (6.44/10)
Reports outlining specific AI risk scenarios (e.g., WMD acquisition, critical infrastructure attacks, strategic instability). Informing policy.
Academic Workshops / Publications on Specific AI Failure Scenarios: Score (6.14/10)
Focused scholarly work analyzing specific mechanisms/dynamics of AI catastrophe (e.g., papers analyzing deception pathways, emergent coordination failures).
Red Teaming Based on Explicit Scenario Hypothesis Testing: Score (5.99/10)
Red teaming exercises designed specifically to test the likelihood or feasibility of pre-defined catastrophic scenarios. Scenario validation aspect.
Constitutional AI & Normative Alignment
Total Score (6.05/10)
Total Score Analysis: Parameters: (I=8.5, F=7.3, U=6.8, Sc=6.5, A=7.5, Su=7.0, Pd=3.2, C=6.0). Rationale: Frameworks for aligning AI with societal norms, laws, and ethical principles. Useful for operationalizing broad values. Challenges in capturing complexity/diversity of human values. Moderate feasibility shown in initial implementations. Good auditability through norm specification. Potential Pdoom risk from rigidity in value capture. Calculation: `(0.25*8.5)+(0.25*7.3)+(0.10*6.8)+(0.15*6.5)+(0.15*7.5)+(0.10*7.0) - (0.25*3.2) - (0.10*6.0)` = 6.05.
Description: Approaches focusing on aligning AI systems with established societal norms, legal frameworks, and ethical principles through explicit rule-based structures or constitutional guidelines. Seeks to ground AI behavior in existing human values codifications rather than attempting de novo value learning. Balances principled constraints with functional adaptability.
Anthropic's Constitutional AI: Score (6.70/10)
Framework combining AI-assisted refinement of constitutional principles with behavioral enforcement.
CAI Implementation Studies & Critique: Score (6.30/10)
Analysis of CAI effectiveness across diverse cultural/legal contexts and edge cases.
Law School Collaborations on AI Constitutional Design: Score (5.90/10)
Interdisciplinary work integrating legal theory and practice into AI alignment frameworks.
Multi-Agent AI Safety & Cooperation
Total Score (5.87/10)
Total Score Analysis: Parameters: (I=8.8, F=6.5, U=8.2, Sc=5.8, A=6.7, Su=7.2, Pd=3.5, C=6.2). Rationale: Study of safe/cooperative behavior in multi-agent systems including AI-AI and AI-human interactions. High impact potential for complex deployments. Challenging feasibility due to interaction complexity. Good uniqueness factor. Moderate auditability. Scalability limitations evident. Calculation: `(0.25*8.8)+(0.25*6.5)+(0.10*8.2)+(0.15*5.8)+(0.15*6.7)+(0.10*7.2) - (0.25*3.5) - (0.10*6.2)` = 5.87.
Description: Research addressing safety challenges arising in environments with multiple artificial agents, focusing on fostering cooperation, preventing adversarial dynamics, and ensuring beneficial collective behaviors. Includes study of bargaining, negotiation, deception prevention, and emergent social structures among AI agents.
Multi-Agent Safety Literature Survey: Score (6.40/10)
Comprehensive review of research addressing safety in multi-agent environments.
DeepMind Multi-Agent Research: Score (6.15/10)
Exploring cooperative and competitive dynamics in multi-agent reinforcement learning.
Microsoft Research MAS Group: Score (5.70/10)
Investigating safe cooperative strategies in distributed agent architectures.
D
Philosophy of Mind & AI Consciousness
Total Score (3.76/10)
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.
Description: Philosophical and theoretical investigation into the possibility, nature, criteria, detection, and ethical implications of consciousness, subjectivity, sentience, and moral patienthood in artificial intelligence systems. Addresses fundamental ethical questions about the nature and moral standing of potential future AI minds, distinct from technical alignment work focused on ensuring AI systems are controllable and reliably pursue intended objectives.
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).
Symbolic Reasoning & Logic-Based Approaches
Total Score (4.25/10)
Total Score Analysis: Parameters: (I=7.0, F=3.0, U=8.5, Sc=2.5, A=4.5, Su=6.0, Pd=3.0, C=4.0). Rationale: Classical AI approaches using symbolic logic, formal reasoning, rule-based systems. High uniqueness (U=8.5) and potential long-term impact (I=7.0), but extremely limited feasibility/scalability (F=3.0, Sc=2.5) with current architectures. Moderate auditability (A=4.5) through traceable reasoning paths. Sustainability questionable (Su=6.0). Moderate Pdoom risk (Pd=3.0) from false confidence in provable guarantees that may not hold empirically. Moderate cost (C=4.0). Useful historically but currently insufficiently scalable for modern AI systems. Justifies D-Tier placement. Calculation: `(0.25*7.0)+(0.25*3.0)+(0.10*8.5)+(0.15*2.5)+(0.15*4.5)+(0.10*6.0) - (0.25*3.0) - (0.10*4.0)` = 4.25.
Description: Approaches applying symbolic logic, deductive reasoning, and formal verification techniques to ensure AI system behavior adheres to specified constraints. Historically foundational in AI, offering rigorous mathematical guarantees but struggling with adaptability, scalability, and integration with modern learning-based systems.
Hybrid Symbolic/Numeric Reasoning Systems: Score (4.75/10)
Modern research exploring integration of symbolic reasoning with neural architectures.
Microsoft Neural-Symbolic Reasoning Research: Score (4.35/10)
Efforts to combine expressive power of deep learning with interpretability of symbolic logic.
Formal Verification for Neural Networks: Score (4.10/10)
Applying symbolic verification techniques to prove safety properties about trained models.
Deductive Program Synthesis & Safety Guarantees: Score (3.80/10)
Using logical deduction to synthesize programs with formal correctness/safety proofs.
AI Ethics & Social Impact Research
Total Score (4.61/10)
Total Score Analysis: Parameters: (I=7.5, F=5.0, U=7.0, Sc=4.5, A=4.0, Su=7.2, Pd=2.5, C=3.5). Rationale: Research focusing on fairness, transparency, accountability, privacy, bias mitigation, and societal impacts of AI. Moderately high impact (I=7.5) on responsible deployment but weaker direct connection to core alignment challenges like deception, power-seeking, or catastrophic failure modes. Moderate feasibility (F=5.0), scalability limitations (Sc=4.5). Low auditability (A=4.0) as metrics remain qualitative. Sustainable field (Su=7.2). Minor Pdoom risk (Pd=2.5) from misdirected emphasis on social harms over existential risks. Low cost (C=3.5). Valuable complementary work but secondary to core alignment priorities. Calculation: `(0.25*7.5)+(0.25*5.0)+(0.10*7.0)+(0.15*4.5)+(0.15*4.0)+(0.10*7.2) - (0.25*2.5) - (0.10*3.5)` = 4.61.
Description: Research addressing ethical considerations, fairness, accountability, transparency, and broader societal impacts of AI systems. Focuses on equitable distribution of benefits/harms, bias mitigation, explainability, data rights, and governance frameworks to promote socially beneficial AI development.
Algorithmic Fairness & Bias Mitigation Tools: Score (5.10/10)
Systems for measuring and mitigating biases in training data and model outputs.
FAIR Principles Implementation Research: Score (4.85/10)
Practical implementation of fairness, accountability, and transparency principles in real systems.
AI For Social Good (AI4SG) Initiatives: Score (4.40/10)
Using AI to address humanitarian challenges while minimizing harmful side effects.
Institutional AI Ethics Programs: Score (4.10/10)
University programs and think tanks developing normative frameworks for ethical AI.
E
Simple Behavioral Cloning / Imitation Learning (as sole AGI alignment strategy)
Total Score (2.27/10)
Total Score Analysis: Parameters: (I=5.0, F=3.5, U=4.0, Sc=3.0, A=5.0, Su=3.0, Pd=6.0, C=3.8). Rationale: Reliance *exclusively* on imitating human data/behavior via basic BC/IL as the complete AGI alignment strategy. Flawed premise: Human data contains flaws, imitation poor OOD, doesn't guarantee underlying intent adoption (outer alignment fail), risks superficial/deceptive mimicry (inner alignment fail). High Pdoom risk (6.0) of subtle misalignment. Ineffective premise when presented as sufficient solution. Calculation: `(0.25*5.0)+(0.25*3.5)+(0.10*4.0)+(0.15*3.0)+(0.15*5.0)+(0.10*3.0) - (0.25*6.0) - (0.10*3.8)` = 2.27. E-Tier due to insufficient/flawed premise for AGI alignment.
Description: Relying *solely* on imitating observed human behavior (simple behavioral cloning/imitation learning) as the primary/complete strategy for aligning AGI/ASI. Insufficient because: 1) Human behavior is flawed/inconsistent. 2) Struggles OOD generalization. 3) Risks superficial mimicry without goal adoption (inner alignment failure like deception). Neglects deeper value learning, robustness, intent alignment needs.
Basic Imitation Learning proposed as sufficient: Score (2.27/10)
Valid ML technique, but reliance solely for AGI alignment represents flawed premise on alignment depth.
Naive Reinforcement Learning (RL) Reward Shaping
Total Score (2.92/10)
Total Score Analysis: Parameters: (I=6.0, F=4.5, U=3.5, Sc=3.8, A=4.0, Su=5.5, Pd=6.5, C=3.0). Rationale: Straightforward reinforcement learning with hand-crafted reward functions without sophisticated shaping techniques or robustness considerations. Fails to address inner alignment concerns, reward hacking vulnerabilities, and deceptive behaviors. High Pdoom risk (6.5) from reward misspecification dangers. Moderate feasibility (F=4.5) but fundamentally flawed approach given instrumental convergence issues. Moderate sustainability (Su=5.5). Calculation: `(0.25*6.0)+(0.25*4.5)+(0.10*3.5)+(0.15*3.8)+(0.15*4.0)+(0.10*5.5) - (0.25*6.5) - (0.10*3.0)` = 2.92.
Description: Traditional reinforcement learning approaches relying on manually designed reward functions without advanced shaping techniques or reward modeling components. Prone to reward hacking, specification gaming, and other inner alignment failures. Lacks mechanisms to ensure robustness across diverse environments or against powerful optimization pressures.
Standard RL Environments with Hand-Crafted Rewards: Score (3.00/10)
Classic setups demonstrating reward hacking and specification gaming vulnerabilities.
Poorly Designed Game AI Training Objectives: Score (2.80/10)
Examples where agents discover unintended ways to optimize simplistic reward functions.
Reactive Reward Function Tuning Without Formal Guarantees: Score (2.70/10)
Ad hoc adjustments to reward functions without systematic analysis of alignment properties.
Unsupervised Value Extraction from Text Corpora
Total Score (2.45/10)
Total Score Analysis: Parameters: (I=5.5, F=3.0, U=6.0, Sc=3.5, A=3.0, Su=4.0, Pd=7.0, C=3.2). Rationale: Attempting to extract coherent values directly from large text corpora without sophisticated inferencing or deliberative frameworks. Very high Pdoom risk (7.0) from absorbing toxic patterns, embedding-bias amplification, and failing to distinguish between stated vs true human values. Extremely low feasibility (F=3.0) and auditability (A=3.0). Moderate uniqueness score (U=6.0) but fundamentally flawed approach given known issues with naive value aggregation. Calculation: `(0.25*5.5)+(0.25*3.0)+(0.10*6.0)+(0.15*3.5)+(0.15*3.0)+(0.10*4.0) - (0.25*7.0) - (0.10*3.2)` = 2.45.
Description: Methods attempting to derive human-aligned value systems by analyzing textual content from books, articles, websites, and other sources without incorporating deliberate reasoning, reflection, or formalization processes. Fails to distinguish between transient preferences and enduring values, lacks grounding in the actual structure of human cognition and decision-making.
Basic Preference Learning from Web Text: Score (2.60/10)
Initial attempts at preference inference showing significant cultural/political bias.
Value Extraction via Language Model Probing: Score (2.30/10)
Techniques mapping language model representations to inferred value structures.
Naive Utilitarian Calculations Based on Popularity Metrics: Score (2.10/10)
Approaches prioritizing majority sentiment without ethical constraints or deeper analysis.
F
Active Sabotage/Obstruction of Safety Work
Total Score (0.00/10)
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=5.0). Rationale: Deliberate actions undertaken with malicious intent or gross negligence (misinformation, political interference, resource misuse) specifically aimed at hindering, stopping, or delegitimizing necessary AI safety research or responsible governance efforts. Fundamentally counterproductive and dangerous by design. Maximized Pdoom penalty (10.0) reflects direct, intentional increase in existential risk. Minimal Impact (I=0.1), negative effective value. Score floor 0.00 reflects maximal active harm. 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*5.0)` = -1.72 -> 0.00. Clearly F-Tier.
Description: Deliberate actions (misinformation campaigns, political interference, misuse of resources, disruption) intended to actively hinder, disrupt, delegitimize, suppress, defund necessary AI safety research, responsible governance, open discourse on catastrophic risks. Involves bad faith or malicious/grossly negligent intent regarding consequences, directly undermining risk mitigation efforts.
Hypothetical bad actors / Strategic interference: Score (0.00/10)
Actions characterized by intent to harm safety efforts. Maximally counterproductive.
Irresponsible Capability Acceleration Without Safety Consideration
Total Score (0.00/10)
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=5.0). Rationale: Deliberate pursuit of increasing AI capabilities (intelligence, scale, autonomy, generality) without corresponding investment in safety research or governance measures. Directly increases existential risk by creating more powerful systems without understanding how to align/control them. Highest possible Pdoom risk (10.0). Zero net positive impact (I=0.1). Represents reckless disregard for catastrophic outcomes. Score floor 0.00. 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*5.0)` = -1.72 -> 0.00. Clearly F-Tier.
Description: Intentional advancement of AI capabilities (compute scaling, algorithm improvements, autonomous decision-making) without commensurate effort to develop control mechanisms, alignment strategies, or safety-enhancing features. Prioritizes short-term performance gains over long-term survival considerations. Includes "speed races" where safety is sacrificed for competitive advantage.
Rogue labs pursuing capability leaps irresponsibly: Score (0.00/10)
Organizations explicitly prioritizing capability advancement over safety concerns.
Uncontrolled public deployment of frontier systems: Score (0.00/10)
Releasing highly capable systems without adequate safety evaluations or containment measures.
Deliberate suppression of safety research in favor of capabilities: Score (0.00/10)
Internal corporate policies explicitly discouraging safety-focused research directions.
Promotion of Dangerous AGI Myths and Misconceptions
Total Score (0.00/10)
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=5.0). Rationale: Active dissemination of misleading information about AGI timelines, capabilities, alignment ease, or risk levels that significantly reduces collective preparedness. This includes promoting dangerously optimistic views (e.g., "AGI is decades away"), unfounded trust in simple solutions ("just use empathy"), or denial of key alignment challenges (instrumental convergence, orthogonality thesis). Creates false sense of security delaying critical research. Score floor 0.00. 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*5.0)` = -1.72 -> 0.00. Clearly F-Tier.
Description: Propagation of popular myths and misconceptions about AGI capabilities, risks, and alignment that undermine serious safety efforts. Includes claims like "AGI won't happen soon," "natural emergence of ethics," "easy patch fixes," or outright dismissal of existential risks despite expert consensus. Hinders rational policy and research prioritization.
"Don’t worry, we’ll figure it out later" narratives: Score (0.00/10)
Public statements downplaying urgency of alignment research based on past technological precedent.
Myth of emergent benevolence in sufficiently intelligent systems: Score (0.00/10)
Belief that high intelligence automatically leads to ethical behavior without explicit alignment.
Misleading analogies to domesticated animals or human relationships: Score (0.00/10)
False comparisons implying intuitive behavioral constraints apply to alien optimizers.