Why responsible ML prevents harm in ML Python - Why Metrics Matter

Metrics & Evaluation - Why responsible ML prevents harm

Which metric matters and WHY

When we talk about responsible machine learning, the key metrics are fairness, accuracy, precision, and recall. Fairness ensures the model treats all groups equally, avoiding harm to any group. Accuracy tells us how often the model is right overall. Precision tells us how many predicted positives are actually positive. Recall is important when missing a positive case causes harm, like in medical diagnosis. These metrics help us check if the model is safe and fair to use in real life.

Confusion matrix example

          Predicted Positive   Predicted Negative
Actual Positive       80                 20
Actual Negative       10                 90

Total samples = 200

True Positives (TP) = 80
False Positives (FP) = 10
True Negatives (TN) = 90
False Negatives (FN) = 20

This matrix helps us calculate precision, recall, and accuracy to understand model performance and potential harm.

Precision vs Recall tradeoff with examples

Imagine a spam email filter. High precision means most emails marked as spam really are spam, so you don't lose important emails. High recall means catching most spam emails, but you might mark some good emails as spam.

In medical tests, high recall is critical to catch all sick patients, even if some healthy people get extra tests (lower precision). Responsible ML balances these metrics to reduce harm depending on the situation.

Good vs Bad metric values for responsible ML

• Good: High recall and precision, balanced fairness across groups, no bias in errors.
• Bad: High accuracy but low recall (missing many positive cases), unfair errors affecting certain groups more, biased predictions causing harm.

Common pitfalls in metrics for responsible ML

• Accuracy paradox: High accuracy can hide poor performance on minority groups.
• Data leakage: When test data leaks into training, metrics look better but model fails in real life.
• Overfitting: Model performs well on training data but poorly on new data, causing unexpected harm.
• Ignoring fairness: Good overall metrics but unfair treatment of some groups can cause harm.

Self-check question

Your model has 98% accuracy but only 12% recall on fraud cases. Is it good for production? Why or why not?

Answer: No, it is not good. Even though accuracy is high, the model misses 88% of fraud cases (low recall). This means many frauds go undetected, causing harm. For fraud detection, high recall is critical to catch as many frauds as possible.

Key Result

Responsible ML focuses on fairness, recall, precision, and accuracy to prevent harm by ensuring models are both correct and fair.

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