Recommendations for Technical AI Safety Research Directions

Anthropic's Alignment Science team has published a research agenda addressing technical approaches to mitigating catastrophic AI risks. The document serves as a "tasting menu" of open problems rather than an exhaustive list.

Core Research Areas

Evaluating Capabilities & Alignment

The team identifies a gap between current AI benchmarks and real-world impact. They note that "many AI capability benchmarks...saturate quickly" and fail to provide meaningful signals about progress. For alignment measurement, existing approaches focus on surface-level properties like refusing harmful queries, yet the team emphasizes the need to understand deeper questions: whether models possess hidden goals, fake alignment, or strategically conceal capabilities.

Understanding Model Cognition

Research should explore what models are "thinking" during generation. This includes investigating whether models form plans, what situational knowledge they possess, and what information they withhold. The team highlights multiple approaches beyond mechanistic interpretability, including chain-of-thought reasoning and training models to "directly verbalize the content of their hidden states."

AI Control Through Monitoring

Three monitoring strategies receive attention:

• Behavioral monitoring: Using auxiliary AI systems to screen inputs/outputs
• Activation monitoring: Detecting dangerous concepts in internal model processing
• Anomaly detection: Flagging unusual or out-of-distribution computations

Scalable Oversight Challenges

For advanced systems operating beyond human expertise, the team identifies three core problems:

1. Noisy oversight from genuinely difficult problems
2. Systematic errors humans might not recognize
3. Expensive specialized evaluation requirements

They propose several solutions: recursive oversight using AI assistance, weak-to-strong generalization (training strong models with weak supervisors), and detecting model honesty through internal representations rather than external verification.

Additional Research Directions

• Adversarial robustness: Developing realistic jailbreak benchmarks measuring differential harm rather than mere non-refusal
• Adaptive defenses: Creating safeguards responding to attacker behavior through inter-query monitoring
• Unlearning: Removing dangerous capabilities so models "behave near-identically to models that were never trained" on that information
• Multi-agent governance: Addressing coordination failures when multiple AI systems interact

Key Emphasis

The research agenda prioritizes work that future AI developers will find practically useful. As the authors note, this collection represents areas "we would like to see progress in, but don't have the capacity to invest in ourselves."