AI Ethics Quiz: Testing Your Knowledge of Real-World Case Studies

📚 Quick Study Guide

• ⚖️ Fairness: Ensuring AI systems do not discriminate against individuals or groups based on protected characteristics.
• 🛡️ Privacy: Protecting sensitive data and complying with regulations like GDPR.
• 📢 Transparency: Making AI decision-making processes understandable and explainable.
• 🤖 Accountability: Establishing responsibility for the actions and outcomes of AI systems.
• 🔒 Security: Protecting AI systems from malicious attacks and unauthorized access.
• 💡 Beneficence: Ensuring that AI systems are developed and used for the benefit of humanity.
• ⚠️ Non-Maleficence: Avoiding the use of AI systems in ways that could cause harm.
• 🌐 Case Study Examples: Review instances like COMPAS recidivism prediction, Amazon's biased hiring tool, and facial recognition controversies.

🧪 Practice Quiz

1. Which ethical concern was highlighted by the COMPAS recidivism prediction algorithm?
   1. A) Lack of transparency in its decision-making process.
   2. B) Bias against certain racial groups.
   3. C) Inadequate security measures to protect sensitive data.
   4. D) Failure to comply with GDPR regulations.
2. What was the primary ethical issue associated with Amazon's AI recruiting tool?
   1. A) Violation of user privacy.
   2. B) Bias against female candidates.
   3. C) Lack of accountability for hiring decisions.
   4. D) Insufficient data encryption.
3. Which ethical principle is most directly violated when an AI system makes decisions without human oversight in critical applications?
   1. A) Fairness.
   2. B) Transparency.
   3. C) Accountability.
   4. D) Privacy.
4. What is 'explainable AI' (XAI) primarily designed to improve?
   1. A) The efficiency of AI algorithms.
   2. B) The accuracy of AI predictions.
   3. C) The transparency and understandability of AI decision-making.
   4. D) The security of AI systems.
5. Which of the following best describes the ethical concern related to using facial recognition technology in public spaces?
   1. A) Potential for mass surveillance and privacy violations.
   2. B) Inability to accurately identify individuals from diverse ethnic backgrounds.
   3. C) High computational cost of running the algorithms.
   4. D) Lack of standardization in data formats.
6. A self-driving car must choose between hitting a pedestrian or swerving and potentially harming its passengers. Which ethical dilemma does this scenario represent?
   1. A) The trolley problem.
   2. B) The prisoner's dilemma.
   3. C) The halting problem.
   4. D) The Byzantine generals problem.
7. What is a key challenge in ensuring fairness in AI systems trained on biased historical data?
   1. A) Biased data inevitably leads to biased outcomes.
   2. B) It is impossible to detect bias in large datasets.
   3. C) Mitigating bias often reduces the accuracy of the AI model.
   4. D) Fairness metrics are universally agreed upon and easy to implement.