Why responsible ML prevents harm in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to print the main goal of responsible ML.

ML Python

print("The main goal of responsible ML is to prevent [1].")

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

Aharm

Baccuracy

Ccomplexity

Dspeed

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'accuracy' because it sounds important, but harm prevention is the key.

Responsible ML aims to prevent harm caused by biased or unfair models.

2fill in blank

medium

Complete the code to check if a model is accurate by comparing predictions.

ML Python

if all(model.predict(data) == [1]):
    print("Model is accurate")

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

Alabels

Bdata

Cfeatures

Dbias

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'data' instead of 'labels' which is incorrect for comparison.

We compare model predictions to true labels to check accuracy.

3fill in blank

hard

Fix the error in the code that checks for bias in predictions.

ML Python

bias_detected = any(prediction [1] threshold for prediction in predictions)

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

A<

B=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' or '==' which are assignment or equality, not comparison.

We check if any prediction is greater than a threshold to detect bias.

4fill in blank

hard

Fill both blanks to create a dictionary of features and their importance if importance is above 0.5.

ML Python

important_features = {feature: [1] for feature, importance in feature_importances.items() if importance [2] 0.5}

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

Aimportance

Bfeature

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using feature as value or using '<' instead of '>'.

We keep features with importance greater than 0.5 and map feature to its importance.

5fill in blank

hard

Fill all three blanks to filter data points where age is above 18 and income is above 30000.

ML Python

filtered_data = [[1] for [2] in data if [3]['age'] > 18 and [3]['income'] > 30000]

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

Apoint

Bx

Citem

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently or wrong variable for list element.

We iterate over data with variable 'point' and select 'point' for the list, using 'point' to access fields.

Practice

1. Why is responsible machine learning important in AI development?

easy

A. It guarantees 100% accuracy in predictions.

B. It makes models run faster without errors.

C. It helps prevent harm by ensuring fairness and safety.

D. It removes the need for human oversight.

Solution

1. Step 1: Understand the goal of responsible ML

   Responsible ML focuses on fairness and safety to avoid harm.

2. Step 2: Compare options to this goal

   Only It helps prevent harm by ensuring fairness and safety. mentions preventing harm by fairness and safety, matching the goal.

3. Final Answer:

   It helps prevent harm by ensuring fairness and safety. -> Option C

4. Quick Check:

   Responsible ML = Prevents harm [OK]

Hint: Responsible ML = fairness + safety to avoid harm [OK]

Common Mistakes:

• Thinking responsible ML guarantees perfect accuracy
• Believing it removes human oversight
• Confusing speed improvements with harm prevention

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

easy

A. Ignoring data biases to speed up training

B. Deploying models without monitoring

C. Using only one data source without checks

D. Regularly testing models for fairness

Solution

1. Step 1: Identify responsible ML practices

   Responsible ML includes testing models regularly to ensure fairness.

2. Step 2: Match options to responsible ML practices

   Regularly testing models for fairness matches this practice; others ignore bias or skip monitoring.

3. Final Answer:

   Regularly testing models for fairness -> Option D

4. Quick Check:

   Testing for fairness = Responsible ML [OK]

Hint: Test models often to catch bias early [OK]

Common Mistakes:

• Ignoring bias in data
• Skipping model monitoring after deployment
• Assuming one data source is enough

3. Consider this code snippet checking model fairness:

def check_fairness(scores):
    return all(score >= 0.8 for score in scores)

results = check_fairness([0.9, 0.85, 0.7, 0.95])
print(results)

What is the output?

medium

A. False

B. None

C. Error

D. True

Solution

1. Step 1: Understand the function check_fairness

   The function returns True only if all scores are at least 0.8.

2. Step 2: Evaluate the scores list

   The list has a 0.7 score, which is less than 0.8, so all() returns False.

3. Final Answer:

   False -> Option A

4. Quick Check:

   All scores >= 0.8? No, so False [OK]

Hint: all() returns False if any item fails condition [OK]

Common Mistakes:

• Assuming all scores are above 0.8
• Confusing any() with all()
• Expecting an error due to code structure

4. This code aims to monitor model bias but has a bug:

def monitor_bias(data):
    for value in data:
        if value < 0.5:
            print("Bias detected")
        else:
            print("No bias")

monitor_bias([0.6, 0.4, 0.7, 0.3])

What is the bug and how to fix it?

medium

A. The function should return a list instead of printing; fix by collecting results.

B. The comparison operator is wrong; use > instead of <.

C. The loop variable is unused; replace 'value' with 'data'.

D. The function is missing a return statement; add 'return None'.

Solution

1. Step 1: Identify the issue with printing inside the loop

   Printing inside the loop does not allow further analysis or storage of bias results.

2. Step 2: Suggest collecting results for monitoring

   Fix by storing results in a list and returning it for better monitoring.

3. Final Answer:

   The function should return a list instead of printing; fix by collecting results. -> Option A

4. Quick Check:

   Print vs return affects monitoring [OK]

Hint: Return data for monitoring, don't just print [OK]

Common Mistakes:

• Changing comparison operator incorrectly
• Confusing variable names
• Adding unnecessary return None

5. A company wants to deploy an AI model for loan approval. To prevent harm, which combination of responsible ML steps is best?

hard

A. Use a simple model without testing, rely on manual checks

B. Use diverse training data, test for bias, monitor model after deployment

C. Train only on past approved loans, deploy quickly, ignore feedback

D. Deploy model without data checks but monitor user complaints

Solution

1. Step 1: Identify responsible ML steps to prevent harm

   Responsible ML requires diverse data, bias testing, and ongoing monitoring.

2. Step 2: Evaluate options for these steps

   Use diverse training data, test for bias, monitor model after deployment includes all key steps; others skip important parts or ignore feedback.

3. Final Answer:

   Use diverse training data, test for bias, monitor model after deployment -> Option B

4. Quick Check:

   Diversity + testing + monitoring = Responsible ML [OK]

Hint: Combine data checks, bias tests, and monitoring [OK]

Common Mistakes:

• Ignoring data diversity
• Skipping bias testing
• Not monitoring after deployment