Why responsible AI development matters in Prompt Engineering / GenAI - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is fairness important in AI systems?

Imagine an AI system used to decide who gets a loan. Why must this system be fair?

A Because unfair AI can deny loans to certain groups without good reason, causing harm.

B Because fairness makes the AI run faster and use less memory.

C Because fairness means the AI always gives loans to everyone regardless of risk.

D Because fairness allows the AI to ignore user data and make random decisions.

💡 Hint

Think about the impact of biased decisions on people's lives.

Fairness ensures AI treats people equally and avoids harm from biased decisions.

❓ Metrics

intermediate

Which metric helps detect bias in classification models?

You have a model that predicts if a person qualifies for a job. Which metric helps check if the model is biased against a group?

A Accuracy score over the entire dataset.

B Number of layers in the model.

C False positive rate difference between groups.

D Training loss value.

💡 Hint

Bias often shows as different error rates for different groups.

False positive rate difference reveals if one group is wrongly flagged more often, indicating bias.

🔧 Debug

advanced

What error occurs with this AI fairness check code?

Consider this Python code snippet that tries to compute demographic parity difference but fails:

group_0 = predictions[labels == 0]
group_1 = predictions[labels == 1]
parity_diff = abs(group_0.mean() - group_1.mean())
print(parity_diff)

What error will this code raise if labels is a list, not a numpy array?

Prompt Engineering / GenAI

import numpy as np
predictions = np.array([0,1,1,0,1])
labels = np.array([0,1,0,1,0])
group_0 = predictions[labels == 0]
group_1 = predictions[labels == 1]
parity_diff = abs(group_0.mean() - group_1.mean())
print(parity_diff)

A IndexError: list index out of range

B AttributeError: 'list' object has no attribute 'mean'

C No error, prints the parity difference

D TypeError: list indices must be integers or slices, not list

💡 Hint

Think about what happens when you use a boolean mask on a Python list.

Boolean indexing works on numpy arrays but not on Python lists, causing a TypeError.

❓ Model Choice

advanced

Which model type is best to improve AI transparency?

You want an AI model that is easy to understand and explain to users. Which model type is best?

A Deep neural network with many hidden layers

B Decision tree with clear branching rules

C Ensemble of random forests

D Support vector machine with RBF kernel

💡 Hint

Think about which model shows decisions in simple steps.

Decision trees show clear rules, making them easier to explain than complex models.

🧠 Conceptual

expert

Why is continuous monitoring important in responsible AI?

After deploying an AI system, why must we keep monitoring its behavior over time?

A Because AI models can degrade or become biased as data changes over time.

B Because monitoring increases the AI model's training speed.

C Because monitoring allows the AI to automatically fix bugs without human help.

D Because once deployed, AI models never change and need no checks.

💡 Hint

Think about how real-world data can shift and affect AI decisions.

Data and environments change, so AI models can become less accurate or unfair without monitoring.

Practice

1. Why is responsible AI development important when AI systems affect people's lives?

easy

A. To increase the number of AI features quickly

B. To ensure AI decisions are fair and do not harm individuals

C. To make AI run faster and use less memory

D. To reduce the cost of AI hardware

Solution

1. Step 1: Understand the impact of AI on people

   AI systems can affect people's lives by making decisions that influence jobs, loans, or healthcare.

2. Step 2: Identify the goal of responsible AI

   Responsible AI aims to make sure these decisions are fair and do not cause harm.

3. Final Answer:

   To ensure AI decisions are fair and do not harm individuals -> Option B

4. Quick Check:

   Responsible AI = fairness and safety [OK]

Hint: Focus on fairness and safety when AI affects people [OK]

Common Mistakes:

• Confusing performance improvements with responsibility
• Ignoring ethical concerns in AI decisions
• Thinking cost reduction is the main goal

2. Which of the following is a correct practice in responsible AI development?

easy

A. Ignoring data bias to speed up training

B. Hiding how AI makes decisions to protect secrets

C. Checking AI decisions for fairness and bias

D. Collecting as much personal data as possible without consent

Solution

1. Step 1: Review responsible AI practices

   Responsible AI includes checking for bias and ensuring fairness in AI decisions.

2. Step 2: Evaluate each option

   Only Checking AI decisions for fairness and bias aligns with responsible AI by checking fairness and bias.

3. Final Answer:

   Checking AI decisions for fairness and bias -> Option C

4. Quick Check:

   Responsible AI = check fairness [OK]

Hint: Look for fairness and bias checks in options [OK]

Common Mistakes:

• Choosing options that ignore bias
• Confusing transparency with secrecy
• Ignoring consent in data collection

3. Consider this code snippet checking AI model fairness:

bias_score = 0.2
if bias_score < 0.3:
    print("Model is fair")
else:
    print("Model is biased")

What will be the output?

medium

A. No output

B. Model is biased

C. SyntaxError

D. Model is fair

Solution

1. Step 1: Understand the condition in the code

   The code checks if bias_score (0.2) is less than 0.3.

2. Step 2: Evaluate the condition and output

   Since 0.2 < 0.3 is true, it prints "Model is fair".

3. Final Answer:

   Model is fair -> Option D

4. Quick Check:

   0.2 < 0.3 = True [OK]

Hint: Compare bias_score with threshold to decide output [OK]

Common Mistakes:

• Confusing less than with greater than
• Thinking code has syntax errors
• Ignoring the print statement

4. This code is meant to check if AI respects privacy by masking sensitive data:

def mask_data(data):
    return data.replace("*", "#")

print(mask_data("user*123"))

What is the error and how to fix it?

medium

A. No error; output is 'user#123'

B. Wrong replace characters; should replace digits, not '*'

C. Function should use .replace('*', '#') but code uses wrong syntax

D. Data masking requires encryption, not replace method

Solution

1. Step 1: Analyze the mask_data function

   The function replaces '*' with '#', and the input string contains '*'.

2. Step 2: Evaluate the output

   The output will be 'user#123', which is the expected masked output.

3. Final Answer:

   No error; output is 'user#123' -> Option A

4. Quick Check:

   Replace method works correctly [OK]

Hint: Check what characters need masking carefully [OK]

Common Mistakes:

• Assuming no error because code runs
• Confusing which characters to replace
• Thinking replace method syntax is wrong

5. You are designing an AI system that recommends loans. Which responsible AI practice should you apply to avoid unfair bias?

hard

A. Test the model on diverse groups and explain decisions clearly

B. Ignore explainability to speed up deployment

C. Collect as much personal data as possible without consent

D. Train the model only on data from one group to simplify

Solution

1. Step 1: Identify risks of bias in loan recommendation

   Using data from only one group or ignoring explainability can cause unfair bias.

2. Step 2: Choose responsible AI practices

   Testing on diverse groups and explaining decisions helps detect and reduce bias.

3. Final Answer:

   Test the model on diverse groups and explain decisions clearly -> Option A

4. Quick Check:

   Diversity and explainability reduce bias [OK]

Hint: Use diverse data and clear explanations to avoid bias [OK]

Common Mistakes:

• Using biased data sets
• Skipping explainability for speed
• Ignoring consent and privacy