Practice - Inherent Challenges - 2.2.3
Practice Questions
Test your understanding with targeted questions
Define bias in the context of machine learning.
💡 Hint: Think about how historical data can influence model behavior.
Name one type of bias and give a brief example.
💡 Hint: Consider how training data reflects real-world demographics.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What term describes systematic prejudice in AI outcomes?
💡 Hint: Think about how real-world biases reflect in data.
Is transparency necessary for trust in AI systems?
💡 Hint: Recall why users need to feel confident in AI applications.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
Design a machine learning model for loan approvals. Identify potential sources of bias and propose strategies to mitigate them. Who would you hold accountable for bias in the outcomes?
💡 Hint: Consider who creates the model and uses the data.
A company using AI for hiring faces backlash due to discrimination claims. What steps should the company take to address accountability and transparency issues?
💡 Hint: Think about both technical solutions and human oversight.
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Reference links
Supplementary resources to enhance your learning experience.