Escaping the Agreement Trap: Defensibility Signals for Evaluating Rule-Governed AI

1. 1Formalize evaluation as policy-grounded correctness to account for multiple valid decisions
2. 2Introduce the Defensibility Index (DI) to quantify the defensibility of AI decisions
3. 3Use DI to distinguish between ambiguity and error in AI decision-making
4. 4Apply policy-grounded correctness to re-evaluate AI systems and avoid the agreement trap
5. 5Compare the performance of AI systems using defensibility signals versus traditional agreement metrics

AI researchers and engineers working on rule-governed AI systems can benefit from this approach to improve evaluation and decision-making

💡 The Agreement Trap penalizes valid decisions and mischaracterizes ambiguity as error, while defensibility signals can improve evaluation and decision-making

🚨 Avoid the Agreement Trap in AI evaluation! 🚨 Introducing Defensibility Index (DI) for policy-grounded correctness #AI #Evaluation