AWS Certified AI PractitionerResponsible AIEasy
A financial institution is developing an AI model to detect fraudulent transactions. The model must be highly accurate and reliable, as false positives could inconvenience legitimate customers, and false negatives could lead to significant financial losses. To ensure the model performs consistently even with novel or slightly altered fraudulent patterns, the development team is rigorously testing its resilience against various adversarial inputs and data shifts. Which Responsible AI concept is being prioritized here?
- AAI robustness
- BAI interpretability
- CAI accountability
- DAI fairness
Show answer & explanationAnswer & explanation
Correct answer: A. AI robustness
Testing resilience against adversarial inputs and data shifts to ensure consistent performance directly addresses AI robustness, which focuses on a model's reliability and security.
Why the other options are wrong
- B. AI interpretability is about understanding decisions, not handling novel patterns securely.
- C. AI accountability is about assigning responsibility, not the model's technical resilience.
- D. AI fairness is about equitable outcomes, not resilience to varied inputs.
AI Robustness
AI robustness refers to an AI system's ability to maintain its performance and reliability even when faced with unexpected, noisy, or adversarial inputs and conditions.
- Ensures system reliability and security.
- Protects against adversarial attacks and data shifts.
- Critical for high-stakes applications like fraud detection.
Memory trick: Strong AI stands firm.