Practice Case Study 2: AI in Automated Hiring and Recruitment – Amplifying Workforce Inequality - 4.2.2 | Module 7: Advanced ML Topics & Ethical Considerations (Weeks 14) | Machine Learning
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4.2.2 - Case Study 2: AI in Automated Hiring and Recruitment – Amplifying Workforce Inequality

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is bias in the context of AI?

💡 Hint: Think about how decisions might favor one group over another.

Question 2

Easy

Give an example of historical bias.

💡 Hint: Consider what happens when an AI learns from a company's history.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is a significant source of bias in AI hiring?

  • A) Algorithms
  • B) Historical Data
  • C) Hardware

💡 Hint: Consider what type of data informs AI decisions.

Question 2

True or False: Representation bias occurs when a hiring system overrepresents certain demographics.

  • True
  • False

💡 Hint: Think about what it means to represent a group adequately.

Solve and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Analyze a case where an AI hiring tool favored candidates from one demographic due to biased historical data. What measures would you recommend to change its design for fairness?

💡 Hint: Consider how different demographics can influence job performance.

Question 2

Develop a strategy for an organization to ensure transparent hiring practices using an AI system. What key elements should be included?

💡 Hint: Think about what information candidates would need to understand AI decisions.

Challenge and get performance evaluation