20.14.2 - Ethical Use of AI in Hazard Prediction
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What is bias in AI?
💡 Hint: Think about data representation.
Why is transparency important?
💡 Hint: Focus on understanding and acceptance.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is important to avoid in AI datasets?
💡 Hint: Think about how data represents different groups.
True or False: Transparency in AI systems is optional.
💡 Hint: Remember the role of users trusting the system.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Analyze a situation where a biased AI system led to adverse consequences in hazard prediction. What measures could have been taken to prevent this?
💡 Hint: Focus on past examples and key measures like data diversity.
Propose a framework for implementing accountability in AI hazard prediction systems. What challenges might arise?
💡 Hint: Consider the complexity of current systems.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.