The complexity of AI systems, like large language models (LLMs), makes handling unexpected inputs a challenge for testers. One crucial aspect of testing is addressing these unpredictable scenarios, as they can make or break applications.
In this week’s article by @amrutapp, “Metamorphic and adversarial strategies for testing AI systems,” discover how these testing techniques can uncover hidden flaws and better prepare AI systems for real-world unpredictability.
What You’ll Learn:
Why edge cases can significantly impact the quality of AI systems and how to address them.
How to test non-deterministic systems by focusing on relationships between inputs and outputs.
How adversarial testing can expose biases and flaws in AI outputs.
How manual and automated testing can work together to analyse patterns, uncover anomalies, and define the limits of AI systems.
After reading, share your thoughts:
- What edge cases or biases have you discovered while testing AI systems?
- What strategies have worked best for preparing AI systems for real-world challenges?