Coursera Spring Sale
40% Off Coursera Plus Annual!
Grab it
Learn to tackle the unique challenges of testing AI applications in this 41-minute conference talk that addresses the complexities of ensuring reliability, safety, and fairness in AI systems. Explore specialized testing approaches for dynamic, data-driven AI models that behave unpredictably in real-world scenarios, covering critical aspects such as handling probabilistic outputs, detecting bias, managing model drift, and ensuring transparency in decision-making processes. Discover how to leverage AI itself to develop better tests and generate synthetic data that creates more realistic testing scenarios than traditional random data approaches. Gain essential skills for confidently validating AI systems whether you're involved in AI development or responsible for system validation, equipping yourself with the knowledge needed to ensure robust and reliable AI solutions in critical applications.
