A Multidimensional Approach to AI Safety: The 3D Risk Map
Coverage of lessw-blog
In a detailed analysis published on LessWrong, the author proposes a structural shift in how we conceptualize AI safety, introducing a three-dimensional framework to categorize the complex landscape of alignment risks.
In a recent post, lessw-blog presents a compelling argument against treating AI alignment as a monolithic challenge. Instead, the author introduces a "3D Map of AI Risk," a conceptual tool designed to locate and characterize specific failure modes based on three distinct axes: Beingness, Cognition, and Intelligence.
The discourse surrounding AI safety often suffers from a lack of precision. Terms like "existential risk" or "misalignment" are frequently used as catch-all phrases that obscure the specific mechanisms by which an AI system might fail or cause harm. As systems grow in complexity, the gap between theoretical safety research and practical engineering widens. This post attempts to bridge that gap by offering a taxonomy that allows researchers to pinpoint where a specific system falls within a risk topology. By distinguishing between attributes like "Beingness" (potentially relating to agency or embodiment) and raw "Intelligence," the framework helps clarify why high-intelligence systems might present different risk profiles depending on their cognitive architecture.
What makes this analysis particularly noteworthy is the methodology used to derive the risk families. Rather than relying solely on existing literature or human intuition, the author employed a bottom-up synthesis using current Large Language Models. By tasking ChatGPT and Gemini to analyze combinations of capabilities across the three axes, the author generated a set of "risk families." Furthermore, these models were instructed to critique each other's groupings, creating a consolidated list through a form of automated adversarial review. This approach leverages current AI capabilities to help map the safety landscape of future systems, reducing individual researcher bias.
The post is accompanied by an interactive 3D visualization, allowing readers to explore the intersections of Beingness, Cognition, and Intelligence. This visualization serves as a practical tool for policymakers and researchers to visualize how increasing capabilities in one dimension without corresponding adjustments in others might shift a system into a high-risk category. It transforms abstract safety concepts into a structured space where specific trajectories can be plotted and analyzed.
For those involved in AI governance or technical safety research, this framework offers a fresh perspective on risk identification. It moves the conversation from abstract philosophy toward a structured engineering problem, providing a necessary scaffold for developing targeted mitigation strategies.
We highly recommend exploring the full methodology and the interactive map to understand the specific risk families identified.
Read the full post on LessWrong
Key Takeaways
- AI alignment is reframed not as a single problem, but as a landscape defined by Beingness, Cognition, and Intelligence.
- The risk taxonomy was generated via a novel methodology using ChatGPT and Gemini to critique and synthesize each other's outputs.
- The framework provides a granular vocabulary for discussing specific failure modes rather than generalized existential risks.
- An interactive 3D visualization is provided to help users explore the relationships between different AI capabilities and risks.