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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is data bias?
💡 Hint: Think about how data representation affects AI.
Question 2
Easy
Name a type of bias that occurs during the labeling process.
💡 Hint: Consider who is annotating the data.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is data bias in AI?
💡 Hint: Focus on how data representation matters.
Question 2
True or False: Algorithmic bias can arise even from well-intentioned data.
💡 Hint: Remember that good intentions do not eliminate the risk of bias.
Solve 2 more questions and get performance evaluation
Push your limits with challenges.
Question 1
Design a strategy to mitigate each type of bias discussed. How would you ensure the AI system remains fair and accountable in its decisions?
💡 Hint: Think about proactive measures you can take at each stage.
Question 2
How do real-world implications of these biases affect how AI is perceived by society? Explore the potential consequences of ignoring these issues.
💡 Hint: Consider societal trust and fairness in technology.
Challenge and get performance evaluation