Benchmarks of Progress vs Benchmarks of Peril: The State of Dangerous Capability Evaluations

This project was one of the top submissions on the (Dec 2024) Writing Intensive course. The text below is an excerpt from the final project.

The majority of benchmarks in AI focus on readily measurable and commercially valuable capabilities: mathematics, programming, logical reasoning, and language understanding. These benchmarks drive progress by providing clear metrics and directly translating to marketable applications, making them highly incentivized targets for companies to optimize against. In contrast, there are far fewer benchmarks for evaluating potentially dangerous capabilities like persuasion, deception, autonomous harmful action, and self-proliferation. This asymmetry exists for two key reasons: companies may be hesitant to demonstrate or draw attention to capabilities that could raise safety concerns, but equally importantly, these capabilities are inherently more challenging to define, standardize, and measure compared to traditional benchmarks. While it's straightforward to measure accuracy on a math test, assessing something like persuasive ability or autonomous harmful action requires complex evaluation protocols and careful consideration of potential risks.

Full project

View the full project here.