How Should AI Be Tested
How should AI be tested? ==AI should be tested in the real context where it will be used—not only on general benchmark data.== [:cite[1]{ln=2}] [:cite[2]{ln=1}] A strong testing process should: 1. Define the purpo...
How should AI be tested? ==AI should be tested in the real context where it will be used—not only on general benchmark data.== [:cite[1]{ln=2}] [:cite[2]{ln=1}] A strong testing process should: 1. Define the purpose and success criteria first. Establish what the model is supposed to achieve and set minimum performance benchmarks before testing begins. Benchmarks can be based on expert human performance, current outcomes, or agreed normative standards. [:cite[3]{ln=2}] [:cite[2]{ln=2}] [:cite[2]{ln=3}] [:cite[2]{ln=4}] 2. Use local, representative data. Validate the model with real input data from the intended deployment context; a model that fails local validation should not be deployed. [:cite[3]{ln=3}] [:cite[2]{ln=1}] 3. Measure more than average accuracy. Testing should compare performance across relevant groups, regions, facilities, and case types, especially where errors could distribute benefits, harms, or risks unfairly. [:cite[1]{ln=4}] 4. Test safety, robustness, and security. Governments should assess whether the system remains dependable under difficult or unusual conditions and whether it creates cybersecurity vulnerabilities. [:cite[5]{ln=1}] [:cite[4]{ln=2}] 5. Test human interaction with the system. Evaluation should monitor whether people use the tool, follow or override its recommendations, face access barriers, or misuse it. [:cite[3]{ln=4}] [:cite[6]{ln=2}] 6. Evaluate real world impact. Testing should examine effects on beneficiaries and nonbeneficiaries, unintended consequences, human well being, and cost effectiveness—not just whether the model produces technically accurate outputs. [:cite[3]{ln=5}] [:cite[7]{ln=3}] 7. Stress test generative AI. Because generative AI can produce different answers to similar prompts and may perform unevenly across tasks, each intended use should be tested with inputs that closely resemble real world use; red teaming can help expose harmful failures. [:cite[8]{ln=2}] [:cite[8]{ln=5}] 8. Continue testing after deployment. Regular revalidation should detect model drift, while day to day monitoring can track user satisfaction, overrides, downtime, help requests, and other signs that the system is or is not working as intended. [:cite[1]{ln=5}] [:cite[6]{ln=2}] [:cite[6]{ln=4}] 9. Document and independently assess results. Performance should be recorded transparently, and governments may work with universities or research institutions to determine whether AI improves outcomes and provides value for money. [:cite[10]{ln=5}] [:cite[9]{ln=6}] [:cite[7]{ln=1}] ==The basic rule is: do not deploy AI because it works in a demonstration; deploy it only when it meets predefined standards in the local setting, performs acceptably across affected groups, and can be monitored and corrected over time.== [:cite[2]{ln=1}] [:cite[1]{ln=4}] [:cite[1]{ln=5}]