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UNIT 8: AI Ethics and Values
UNIT 6: Machine Learning Algorithms
September 13, 2024
Regression in AI
September 13, 2024
AI Ethics and Values
MCQs :
Ethics in AI and Bias:
- What is a primary concern of AI ethics?
A) Speed
B) Fairness
C) Size
D) Popularity
Answer: B - What does transparency in AI mean?
A) Speed of decision making
B) Openness about how AI decisions are made
C) AI cost transparency
D) Increasing user interaction
Answer: B - What is the significance of explainability in AI?
A) Makes AI faster
B) Helps understand how decisions are made
C) Reduces computational cost
D) Eliminates the need for human intervention
Answer: B - What is the effect of bias in AI systems?
A) Faster decision-making
B) Incorrect predictions and unfair outcomes
C) Greater accuracy in results
D) Increased transparency
Answer: B - Which of the following can introduce bias into an AI system?
A) Diverse training data
B) Biased training data
C) Extensive testing
D) Regular auditing
Answer: B - In the context of AI, fairness refers to:
A) Avoiding discrimination in decision-making
B) Speeding up processes
C) Lower costs
D) Better user interfaces
Answer: A - What is cognitive bias in AI?
A) Errors in AI calculations
B) AI systems processing data slower
C) Human biases embedded in AI systems
D) Flaws in hardware components
Answer: C - Which of the following is an example of AI bias?
A) AI accurately predicting stock market trends
B) Facial recognition misidentifying people of color
C) AI operating faster than expected
D) AI creating more jobs
Answer: B - What is algorithmic bias?
A) Errors caused by flawed training data
B) AI functioning in high-risk environments
C) Random errors in AI systems
D) Faster performance in certain conditions
Answer: A - What is one way to mitigate AI bias?
A) Avoiding updates to algorithms
B) Using diverse and representative datasets
C) Reducing transparency
D) Speeding up decision processes
Answer: B
Pillars of AI Ethics - Which is NOT one of the five pillars of AI ethics?
A) Privacy
B) Explainability
C) Transparency
D) Profitability
Answer: D - Explainability in AI helps in:
A) Faster calculations
B) Providing insights into how decisions are made
C) Reducing system costs
D) Eliminating all biases
Answer: B - What does privacy in AI ethics emphasize?
A) Reducing AI training data
B) Protecting personal data and user information
C) Increasing system speed
D) Enhancing decision-making
Answer: B - What does robustness in AI mean?
A) Ability to operate consistently across different conditions
B) Ability to outperform humans
C) Simplifying AI systems
D) Reducing errors to zero
Answer: A - How does fairness in AI help society?
A) Reduces system cost
B) Ensures that decisions are made without bias
C) Makes AI decisions faster
D) Increases complexity
Answer: B
Real-World Examples of AI Bias - Which sector faced an issue with biased AI systems in the US?
A) Healthcare
B) E-commerce
C) Education
D) Agriculture
Answer: A - What was a major cause of bias in AI hiring systems?
A) Poor algorithm design
B) Over-representation of one demographic group
C) Lack of historical data
D) Incorrect cost estimates
Answer: B - What bias was found in online advertising AI?
A) Displaying high-paying jobs to men more often than women
B) Under-representing high-income earners
C) Prioritizing ads for senior citizens
D) Showing luxury products to low-income users
Answer: A - Why is facial recognition AI criticized?
A) It’s too slow
B) It cannot detect facial expressions
C) It misidentifies certain demographic groups, especially minorities
D) It only works indoors
Answer: C - AI systems in predictive policing have been found to:
A) Solve crimes faster
B) Perpetuate racial and socio-economic biases
C) Eliminate all human error in decision making
D) Make accurate crime predictions
Answer: B
AI Policies - Why is developing AI policies important?
A) To ensure faster AI development
B) To promote responsible, safe, and ethical use of AI
C) To increase AI profitability
D) To reduce public scrutiny of AI systems
Answer: B - What is the focus of IBM’s AI Ethics Board?
A) Speed and profitability
B) Ethical development and deployment of AI
C) Increasing AI market share
D) Eliminating human oversight
Answer: B - What does Microsoft emphasize in its Responsible AI framework?
A) Speed
B) Fairness and privacy
C) Eliminating all risks
D) Reducing transparency
Answer: B - Which is a key principle in the European Union’s Ethics Guidelines for Trustworthy AI?
A) Maximizing profits
B) Human autonomy
C) Reducing workforce
D) Accelerating AI adoption
Answer: B - What is a primary role of AI policy?
A) Reducing data privacy
B) Protecting user rights and ensuring responsible AI use
C) Eliminating transparency requirements
D) Increasing costs of AI development
Answer: B
Games and Simulations in AI Ethics - The Moral Machine game helps users explore:
A) AI system failures
B) Ethical dilemmas in decision-making
C) AI profitability
D) Speed of decision-making in AI
Answer: B - In the “Survival of the Best Fit” game, students learn about:
A) Bias and fairness in AI
B) Speed of AI development
C) The cost of AI systems
D) AI design and user interfaces
Answer: A - What ethical issue is highlighted in AI-based autonomous vehicles?
A) Decision-making in life-threatening situations
B) Increased profits for car manufacturers
C) Privacy concerns of drivers
D) Speed limits in city areas
Answer: A - Which is a core aspect of AI ethics games like the Moral Machine?
A) Improving AI speed
B) Highlighting the consequences of ethical decisions
C) Reducing AI costs
D) Simplifying AI algorithms
Answer: B - What is the significance of playing moral dilemma games in AI?
A) To make AI systems faster
B) To promote critical thinking and ethical reasoning
C) To reduce transparency
D) To eliminate the need for testing AI systems
Answer: B
Bias in AI: Sources and Implications - Bias in AI can stem from:
A) Low system cost
B) Flawed training data
C) Faster processing
D) Higher transparency
Answer: B - How can AI perpetuate existing social inequalities?
A) By reducing accuracy
B) By making slow decisions
C) By amplifying biases from historical data
D) By eliminating human oversight
Answer: C - What is the impact of bias in healthcare AI systems?
A) Equitable healthcare for all
B) Lower care standards for minority groups
C) Reduced healthcare costs
D) Faster patient diagnoses
Answer: B - Why are gender biases found in some AI-generated images?
A) Incorrect programming
B) Biased training data representing stereotypical roles
C) Faster image generation techniques
D) Increased system speed
Answer: B - What was a flaw in Amazon’s AI recruiting tool?
A) It was too slow
B) It favored male candidates
C) It eliminated all resumes from minorities
D) It reduced the company’s profitability
Answer: B
Mitigating AI Bias - What is one method to reduce bias in AI?
A) Using a single type of data
B) Using diverse datasets in AI training
C) Reducing algorithm transparency
D) Speeding up decision-making
Answer: B - Fair algorithms aim to:
A) Reduce system costs
B) Eliminate bias and ensure fair outcomes
C) Increase processing speed
D) Maximize profits for developers
Answer: B - Which team structure helps reduce bias in AI development?
A) A homogeneous team
B) A diverse team with varied backgrounds
C) A team focused on speed
D) A team with minimal experience
Answer: B - Transparent AI systems allow users to:
A) Avoid legal scrutiny
B) Understand how AI decisions are made
C) Increase system profitability
D) Simplify system design
Answer: B - How does diverse training data mitigate bias in AI systems?
A) By improving decision-making speed
B) By reducing costs
C) By providing balanced and representative information
D) By making the system less transparent
Answer: C
Privacy and Accountability in AI - Why is privacy important in AI systems?
A) To increase system speed
B) To protect personal data and prevent misuse
C) To reduce system complexity
D) To eliminate human oversight
Answer: B - Who is responsible for decisions made by AI systems?
A) Only the AI system
B) Developers, users, and regulators
C) Only users
D) Only regulators
Answer: B - Accountability in AI means:
A) AI operating without human oversight
B) Clear responsibility for AI decisions
C) Increasing system speed
D) Reducing system cost
Answer: B - What does data privacy in AI focus on?
A) Faster decision making
B) Protecting user information and ensuring responsible data collection
C) Reducing system transparency
D) Eliminating algorithmic bias
Answer: B - A key area in AI policies is:
A) Reducing system speed
B) Protecting user data privacy and ensuring fairness
C) Eliminating human oversight
D) Reducing transparency
Answer: B
Critical Thinking in AI Ethics - Critical thinking in AI ethics encourages:
A) Faster decision-making
B) Evaluation of ethical dilemmas and thoughtful decision-making
C) Reducing transparency
D) Speeding up AI deployment
Answer: B - What is a moral dilemma in AI development?
A) Decisions that impact only AI developers
B) Conflicts between ethical principles with no clear right or wrong
C) Simple decisions about system speed
D) Maximizing profits without considering ethics
Answer: B - How can students use ethical games to learn about AI ethics?
A) By solving ethical dilemmas through interactive scenarios
B) By reducing game complexity
C) By improving AI speed
D) By reducing AI system costs
Answer: A - What type of thinking does the Moral Machine game promote?
A) Faster decision-making
B) Critical thinking and moral reasoning
C) Algorithm simplification
D) Increased transparency
Answer: B - What ethical principle is at play in AI decision-making scenarios like autonomous vehicles?
A) Privacy
B) Accountability
C) Speed of decisions
D) The trade-off between human safety and decision outcomes
Answer: D
ASSERTION-REASONING BASED QUESTIONS:
- Assertion (A): AI systems can perpetuate bias present in training data.
Reason (R): AI systems are entirely objective and cannot be influenced by flawed or biased data.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: C
- Assertion (A): Explainability in AI systems helps increase user trust.
Reason (R): Explainability ensures that AI decisions can be understood and justified by human users.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: A
- Assertion (A): Fairness in AI ensures that decisions are made without bias or discrimination.
Reason (R): Fairness in AI systems can be achieved by using a diverse and representative training dataset.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: A
- Assertion (A): Facial recognition systems have been shown to be equally accurate for all demographic groups.
Reason (R): Biases in training data and algorithms can lead to unequal accuracy in facial recognition systems.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: D
- Assertion (A): AI policies are essential for ensuring the ethical use of AI technologies.
Reason (R): AI policies help regulate issues like bias, privacy, and accountability in AI systems.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: A
- Assertion (A): Cognitive bias can only occur in AI systems if the developers intentionally program it in.
Reason (R): Cognitive bias is often introduced into AI systems through the unconscious preferences of developers and biased data.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: D
- Assertion (A): Algorithmic bias can amplify the biases found in human society.
Reason (R): AI algorithms rely on historical data, which may contain existing societal biases.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: A
- Assertion (A): Transparency in AI systems ensures that the decision-making process remains hidden.
Reason (R): Transparency helps users and stakeholders understand how AI systems make decisions, promoting accountability.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: D
- Assertion (A): Biased AI systems can reinforce existing societal inequalities.
Reason (R): AI systems trained on biased data can produce decisions that favor certain groups over others.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: A
- Assertion (A): Robustness in AI systems is crucial for ensuring consistent performance across different environments.
Reason (R): Robust AI systems are designed to handle errors and operate reliably under various conditions.
A) Both A and R are true, and R is the correct explanation of A.
B) Both A and R are true, but R is not the correct explanation of A.
C) A is true, but R is false.
D) A is false, but R is true.
Answer: A
SHORT-ANSWERED QUESTIONS:
1. What is AI bias, and how does it occur?
Answer:
AI bias occurs when an AI system produces unfair or inaccurate results, often due to flawed or biased training data. This bias can reflect existing societal inequalities and be amplified in the decision-making process.
2. Why is explainability important in AI systems?
Answer:
Explainability in AI systems is crucial because it helps users understand how decisions are made, fostering trust and accountability. It ensures that stakeholders can comprehend the logic and factors driving AI decisions, which is essential for ethical use.
3. What is the role of fairness in AI ethics?
Answer:
Fairness in AI aims to eliminate bias and ensure that decisions made by AI systems are just and do not discriminate against any demographic group. This involves auditing data for bias and diversifying datasets to promote inclusivity.
4. How can cognitive bias be introduced into AI systems?
Answer:
Cognitive bias can be introduced into AI systems through the unconscious preferences of developers and biased data selection. These biases often reflect human tendencies and societal norms, which can influence the outcomes of AI systems.
5. What are the five pillars of AI ethics?
Answer:
The five pillars of AI ethics are:
- Explainability – Making decisions understandable.
- Fairness – Avoiding discrimination and ensuring equitable outcomes.
- Robustness – Ensuring consistent performance across different environments.
- Transparency – Being open about how AI decisions are made.
- Privacy – Protecting personal information and ensuring data security.
6. How does transparency in AI systems promote accountability?
Answer:
Transparency promotes accountability by making the processes and decision-making of AI systems open and understandable to users and stakeholders. This allows for greater scrutiny, evaluation, and trust in the system’s fairness and ethical implications.
7. What is algorithmic bias, and why is it problematic?
Answer:
Algorithmic bias occurs when AI systems produce biased outcomes due to flawed training data or programming. It is problematic because it can perpetuate or even amplify existing inequalities, leading to unfair treatment of certain groups.
8. How can diverse training data mitigate bias in AI?
Answer:
Using diverse training data helps mitigate bias in AI systems by providing a broader, more representative set of examples for the AI to learn from. This reduces the risk of the AI system making biased decisions based on a narrow or unrepresentative dataset.
9. Why is privacy important in AI systems?
Answer:
Privacy is important in AI systems because it protects individuals’ personal information and ensures that data is used responsibly. Ethical AI systems must safeguard data privacy to prevent misuse, unauthorized access, and violations of user rights.
10. What is the significance of robustness in AI systems?
Answer:
Robustness refers to an AI system’s ability to perform reliably across various conditions without errors. It is significant because it ensures that AI systems provide consistent and accurate results, even when faced with different datasets or environments.
11. How do biased AI systems affect healthcare?
Answer:
Biased AI systems in healthcare can lead to unfair treatment recommendations, often providing lower quality care to underrepresented groups such as minorities. This can exacerbate existing healthcare disparities and worsen health outcomes for those groups.
12. What is the Moral Machine game, and what does it teach about AI ethics?
Answer:
The Moral Machine game is an interactive platform developed by MIT that explores ethical dilemmas in AI decision-making, particularly in autonomous vehicles. It teaches users to think critically about moral choices AI systems must make in life-and-death situations.
13. What role do AI policies play in promoting responsible AI use?
Answer:
AI policies establish guidelines for the ethical use, development, and deployment of AI systems. They address key issues such as bias, privacy, transparency, and accountability, ensuring that AI technologies are used responsibly and align with societal values.
14. What are some real-world examples of AI bias?
Answer:
Examples of AI bias include:
- Facial recognition systems misidentifying people of color.
- AI hiring systems that favor male candidates over female ones.
- Healthcare AI providing lower quality care to minority patients due to biased data.
15. How can explainability and transparency reduce mistrust in AI systems?
Answer:
Explainability and transparency reduce mistrust by allowing users to understand how AI systems arrive at their decisions. This openness fosters confidence in the fairness, accuracy, and accountability of AI, making users more likely to trust and adopt AI technologies.
