The project tackles a significant gap in understanding existential risks from artificial agents - AI systems or autonomous entities that might cause large-scale harm either accidentally (through misalignment) or deliberately. Unlike broader AI safety concerns, these "agential s-risks" specifically examine scenarios where harm stems from goal-directed behaviors of autonomous systems, requiring unique approaches to detection and prevention.

Research Framework and Approach

One way to address this could involve creating a specialized research program that combines threat modeling with practical intervention design. The work might develop:

• Framework for categorizing different agent motivations (misalignment, adversarial goals, unintended behaviors)
• Early warning indicators to spot problematic agent behaviors before they escalate
• Case studies analyzing historical incidents where autonomous systems nearly caused harm

The research could bridge theoretical safety concepts with actionable tools for developers, such as risk assessment checklists that integrate seamlessly with existing development workflows.

Applications and Implementation

To make the research impactful, potential applications might include:

• Training programs helping AI teams recognize and mitigate agent-specific risks during development
• Policy guidelines for governing bodies overseeing autonomous system deployment
• Certification standards that incentivize companies to adopt risk assessment practices

The tools and frameworks could be tested through collaborations with AI labs and refined based on real-world feedback. An MVP might start with a simplified risk taxonomy and basic assessment tool, growing more sophisticated as the research progresses.

By focusing specifically on the distinct challenges posed by goal-directed artificial agents, this approach could provide missing pieces in current AI safety efforts while remaining practical enough for industry adoption.