Creating a standard for AI accountability requires more than just a list of questions; it demands a “Gold Standard”methodology. Our Human Rights Benchmark Project is rooted in scientific rigor and deep domain expertise, ensuring we measure true legal understanding, not statistical artifacts.
The Four Pillars of Construct Validity
Our design process, a collaboration between technical AI researchers and human rights experts, adheres to four critical principles:
Real-World Fidelity: The tasks must reflect the actual work human rights professionals do—specifically, monitoring and reporting on potential violations—to ensure the benchmark is useful and relevant. We are not using simplified standardized tests (like a bar exam); we are simulating real-world claims.
Domain Coverage (Taxonomy): We must capture the full range of human rights variation. Our taxonomy divides scenarios into:
Analytic Categories: The nature of the obligation violated (Respect, Protect, Fulfill) and the type of failure (Structural, Process, or Outcome).
Descriptive Categories: The actors involved (State, Company, NGO) and the specific rights holders affected (women, children, indigenous peoples, etc.). This also includes complex scenarios involving AI, conflict, or climate change.
Measurability: Tasks must allow for objective scoring. We break down complex legal concepts into measurable sub-tasks, moving beyond difficult-to-evaluate open-ended text generation where possible.
Appropriate Metrics: We develop nuanced scoring methods that accurately assess an LLM’s response, even allowing for the uncertainty found in expert legal judgment (e.g., slight tolerance for ranking laws where experts might slightly disagree).