32.17.2 - Transparency and Accountability
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 does transparency in AI mean?
💡 Hint: Think about how well we can see the reasons behind decisions.
Define accountability in the context of AI.
💡 Hint: Who is responsible if things go wrong?
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is Explainable AI (XAI)?
💡 Hint: It clarifies the workings of AI.
True or False: Accountability ensures there are no consequences for AI mistakes.
💡 Hint: Think about who is responsible for AI outcomes.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Provide a detailed explanation of how failure to implement transparent AI could impact a civil engineering project.
💡 Hint: Think about the effects on communication and trust.
Analyze the effects of having insufficient audit trails in an AI-driven project.
💡 Hint: Consider what audit trails are meant to accomplish in terms of decision-making clarity.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.