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

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the main reason why responsible AI development is important.

Prompt Engineering / GenAI

print('Responsible AI development helps to ensure that AI systems are [1] and fair.')

Drag options to blanks, or click blank then click option'

Aexpensive

Bbiased

Cslow

Dethical

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'biased' because it is the opposite of fair.

Choosing 'slow' or 'expensive' which are unrelated to ethics.

Responsible AI development focuses on creating AI systems that are ethical, meaning they respect fairness and avoid harm.

2fill in blank

medium

Complete the code to identify a key risk of irresponsible AI development.

Prompt Engineering / GenAI

risk = 'AI systems may [1] certain groups unfairly if not developed responsibly.'

Drag options to blanks, or click blank then click option'

Adiscriminate

Bhelp

Csupport

Dignore

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'help' or 'support' which are positive actions, not risks.

Choosing 'ignore' which does not capture unfair treatment.

Irresponsible AI can discriminate, meaning it treats some groups unfairly based on biased data or design.

3fill in blank

hard

Fix the error in the code that checks if AI development follows responsible principles.

Prompt Engineering / GenAI

if ai_system.is_[1]():
    print('AI is developed responsibly')
else:
    print('AI development needs improvement')

Drag options to blanks, or click blank then click option'

Acheap

Bfast

Cethical

Dcomplex

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fast' or 'cheap' which are unrelated to responsibility.

Using 'complex' which does not indicate ethical behavior.

The method should check if the AI system is ethical, meaning it follows responsible principles.

4fill in blank

hard

Fill both blanks to complete the code that filters AI models based on fairness and transparency.

Prompt Engineering / GenAI

filtered_models = [model for model in models if model.is_[1]() and model.has_[2]()]

Drag options to blanks, or click blank then click option'

Afair

Bfast

Ctransparency

Daccurate

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'fast' or 'accurate' which are not the focus here.

Mixing up fairness and transparency terms.

We filter models that are fair and have transparency, which are key for responsible AI.

5fill in blank

hard

Fill all three blanks to complete the code that creates a report on AI ethics, bias, and accountability.

Prompt Engineering / GenAI

report = {
    '[1]': ai_system.check_ethics(),
    '[2]': ai_system.detect_bias(),
    '[3]': ai_system.ensure_accountability()
}

Drag options to blanks, or click blank then click option'

Aethics

Bbias

Caccountability

Dperformance

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'performance' which is unrelated to responsibility.

Mixing up the order of keys.

The report keys must be 'ethics', 'bias', and 'accountability' to reflect responsible AI checks.

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