AI ethics and responsible usage in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bias in AI Models

Which of the following best describes a common source of bias in AI models?

A Using a training dataset that does not represent all groups fairly

B Choosing a model with too many layers

C Running the model on a slow computer

D Using a programming language that is outdated

💡 Hint

Think about what happens if the data the AI learns from is not balanced.

Bias often comes from training data that lacks fair representation of all groups, causing the AI to perform poorly or unfairly for some people.

❓ Metrics

intermediate

Evaluating Fairness Metrics

You have an AI model that predicts loan approvals. The model approves 90% of applications from one group but only 50% from another. Which metric best helps identify this fairness issue?

A Accuracy

B Demographic parity

C Mean squared error

D Precision

💡 Hint

Look for a metric that compares outcomes across different groups.

Demographic parity measures if different groups receive positive outcomes at similar rates, highlighting fairness issues.

❓ Model Choice

advanced

Choosing Models for Privacy Preservation

You want to build an AI system that respects user privacy by not storing personal data. Which approach is best?

A Use federated learning to train models locally on user devices

B Collect all data and anonymize it after training

C Train a centralized model with all user data stored on one server

D Use a simple linear regression model to reduce data needs

💡 Hint

Consider methods that keep data on user devices instead of sending it to a central place.

Federated learning trains models on user devices without sending personal data to a central server, enhancing privacy.

🔧 Debug

advanced

Identifying Ethical Risks in AI Deployment Code

Consider this pseudocode for deploying an AI chatbot:

if user_input contains sensitive_topic:
    respond with generic answer
else:
    respond with AI-generated answer

What ethical risk does this code most likely introduce?

A It stores all user inputs without consent

B It will always give detailed answers, risking privacy

C It uses too much computing power for simple responses

D It may ignore important user needs by avoiding sensitive topics

💡 Hint

Think about how avoiding sensitive topics might affect users seeking help.

By giving generic answers to sensitive topics, the chatbot may fail to support users who need specific help, causing harm.

🧠 Conceptual

expert

Balancing Transparency and Security in AI Systems

Which statement best explains the challenge of transparency in AI while maintaining security?

A Security requires hiding all AI decision processes from users

B Transparency means AI systems must never be updated after deployment

C Making AI models fully transparent can expose sensitive data or system vulnerabilities

D Transparency and security are unrelated and can be handled separately

💡 Hint

Consider what happens if you reveal everything about how an AI works.

Sharing full details of AI models can risk exposing private data or ways to attack the system, so transparency must be balanced with security.

Practice

1. What is the main goal of AI ethics?

easy

A. To increase AI's data storage

B. To make AI run faster

C. To reduce AI's power consumption

D. To make sure AI is fair, safe, and respects people

Solution

1. Step 1: Understand AI ethics purpose

   AI ethics focuses on fairness, safety, and respect for people when using AI.

2. Step 2: Compare options to this purpose

   Only To make sure AI is fair, safe, and respects people matches this goal; others focus on technical aspects unrelated to ethics.

3. Final Answer:

   To make sure AI is fair, safe, and respects people -> Option D

4. Quick Check:

   AI ethics = fairness and safety [OK]

Hint: Ethics means fairness and safety in AI [OK]

Common Mistakes:

• Confusing ethics with technical performance
• Thinking ethics is about speed or storage
• Ignoring fairness and respect aspects

2. Which of the following is a correct practice to protect user privacy in AI?

easy

A. Collect all user data without consent

B. Share user data publicly for transparency

C. Use data anonymization before training AI

D. Ignore data protection laws

Solution

1. Step 1: Identify privacy protection methods

   Data anonymization removes personal details to protect privacy.

2. Step 2: Evaluate options for privacy respect

   Only Use data anonymization before training AI uses anonymization; others violate privacy or laws.

3. Final Answer:

   Use data anonymization before training AI -> Option C

4. Quick Check:

   Privacy protection = anonymize data [OK]

Hint: Anonymize data to protect privacy [OK]

Common Mistakes:

• Assuming collecting all data is okay
• Confusing transparency with sharing private data
• Ignoring legal rules on data

3. Consider this code snippet that checks for bias in AI predictions:

predictions = ["male", "female", "male", "male", "female"]
if predictions.count("female") / len(predictions) < 0.3:
    print("Bias detected")
else:
    print("No bias")

What will this code print?

medium

A. Bias detected

B. No bias

C. Error: division by zero

D. Error: count method not found

Solution

1. Step 1: Calculate female ratio in predictions

   Count of "female" is 2, total predictions are 5, ratio = 2/5 = 0.4.

2. Step 2: Compare ratio to 0.3 threshold

   0.4 is not less than 0.3, so else branch runs printing "No bias".

3. Final Answer:

   No bias -> Option B

4. Quick Check:

   Female ratio 0.4 > 0.3 means no bias [OK]

Hint: Calculate ratio and compare to threshold [OK]

Common Mistakes:

• Miscounting female occurrences
• Confusing < with > in condition
• Assuming code errors without checking

4. This code aims to log AI decisions for transparency but has an error:

decisions = ["approve", "deny", "approve"]
for i in range(len(decisions))
    print(f"Decision {i}: {decisions[i]}")

What is the error and how to fix it?

medium

A. Missing colon after for loop; add ':' at end of for line

B. Wrong variable name; change 'i' to 'index'

C. Print statement syntax error; remove f-string

D. decisions list is empty; add elements

Solution

1. Step 1: Identify syntax error in for loop

   The for loop line lacks a colon at the end, causing a syntax error.

2. Step 2: Fix syntax by adding colon

   Add ':' after 'range(len(decisions))' to correct the loop syntax.

3. Final Answer:

   Missing colon after for loop; add ':' at end of for line -> Option A

4. Quick Check:

   For loop needs ':' [OK]

Hint: Check for missing colons in loops [OK]

Common Mistakes:

• Changing variable names unnecessarily
• Removing valid f-string formatting
• Assuming list is empty without checking

5. You want to build an AI system that recommends jobs fairly to all genders. Which approach best ensures ethical and responsible usage?

hard

A. Train on balanced data, anonymize gender info, and explain recommendations

B. Use only male data to improve accuracy

C. Ignore fairness to speed up training

D. Share all user data publicly for transparency

Solution

1. Step 1: Identify ethical practices for fairness

   Balanced data avoids bias; anonymizing protects privacy; explanations build trust.

2. Step 2: Evaluate options for responsible AI

   Only Train on balanced data, anonymize gender info, and explain recommendations combines fairness, privacy, and transparency correctly.

3. Final Answer:

   Train on balanced data, anonymize gender info, and explain recommendations -> Option A

4. Quick Check:

   Fairness + privacy + transparency = Train on balanced data, anonymize gender info, and explain recommendations [OK]

Hint: Balance data, protect privacy, explain AI decisions [OK]

Common Mistakes:

• Using biased data sets
• Ignoring privacy laws
• Confusing transparency with sharing private data