Responsible AI practices in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to log model fairness metrics using a popular MLOps tool.

MLOps

from mlflow import [1]

client = [1].MlflowClient()
client.log_metric(run_id, 'fairness_metric', fairness_value)

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

Amodel

Btracking

CMlflow

Dexperiment

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Mlflow' instead of 'tracking' causes import errors.

The tracking module provides the MlflowClient class to log metrics such as fairness.

2fill in blank

medium

Complete the code to check for bias in a dataset using the AI Fairness 360 toolkit.

MLOps

from aif360.datasets import [1]

dataset = [1](features, labels)

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AFairnessDataset

BBiasDataset

CStandardDataset

DMLDataset

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent classes like 'BiasDataset' causes import errors.

StandardDataset is the common class to load datasets for bias checking in AI Fairness 360.

3fill in blank

hard

Fix the error in the code that applies a fairness metric to a model's predictions.

MLOps

from aif360.metrics import [1]

metric = [1](dataset_true, dataset_pred, unprivileged_groups, privileged_groups)
fairness_score = metric.mean_difference()

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

AClassificationMetric

BBiasMetric

CFairnessMetric

DPredictionMetric

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong class names causes attribute errors.

ClassificationMetric is the correct class to compute fairness metrics on classification results.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters features with values greater than 0.

MLOps

{feature: value for feature, value in features.items() if value [1] 0 and feature [2] 'age'}

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

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causes wrong filtering.

Using '==' instead of '!=' includes 'age' feature.

The code filters features where the value is greater than 0 and the feature name is not 'age'.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercased feature names to values greater than 10.

MLOps

{ [1]: [2] for [3], value in features.items() if value > 10 }

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Afeature.upper()

Bvalue

Cfeature

Dval

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'val' instead of 'value' causes NameError.

Not uppercasing feature names changes keys.

The comprehension maps uppercased feature names to their values for values greater than 10.

Practice

1. What is the main goal of Responsible AI practices?

easy

A. To ensure AI systems are fair, safe, and trustworthy

B. To make AI run faster on all devices

C. To increase the complexity of AI models

D. To reduce the cost of AI hardware

Solution

1. Step 1: Understand the purpose of Responsible AI

   Responsible AI focuses on ethical and safe AI use, not speed or cost.

2. Step 2: Identify the key goals

   Fairness, safety, and trustworthiness are the core goals of Responsible AI.

3. Final Answer:

   To ensure AI systems are fair, safe, and trustworthy -> Option A

4. Quick Check:

   Responsible AI = fairness, safety, trust [OK]

Hint: Responsible AI means fairness and safety first [OK]

Common Mistakes:

• Confusing performance optimization with ethical goals
• Thinking cost reduction is the main focus
• Assuming complexity equals responsibility

2. Which of the following is a correct practice to check AI bias in a model?

easy

A. Using fairness metrics to evaluate model outputs

B. Avoiding transparency in model decisions

C. Only testing the model on training data

D. Ignoring data diversity during training

Solution

1. Step 1: Identify bias checking methods

   Bias checks require measuring fairness, not ignoring data or hiding decisions.

2. Step 2: Match correct practice

   Using fairness metrics helps detect bias in model outputs effectively.

3. Final Answer:

   Using fairness metrics to evaluate model outputs -> Option A

4. Quick Check:

   Bias check = fairness metrics [OK]

Hint: Use fairness metrics to spot bias [OK]

Common Mistakes:

• Ignoring diverse data leads to hidden bias
• Testing only on training data misses real bias
• Lack of transparency hides bias issues

3. Consider this Python snippet for monitoring AI model fairness:

fairness_scores = {'groupA': 0.85, 'groupB': 0.65}
if min(fairness_scores.values()) < 0.7:
    alert = 'Bias detected'
else:
    alert = 'Fair model'

What will be the value of alert after running this code?

medium

A. 'Fair model'

B. KeyError

C. TypeError

D. 'Bias detected'

Solution

1. Step 1: Evaluate fairness scores

   Values are 0.85 and 0.65; minimum is 0.65.

2. Step 2: Check condition in if statement

   Since 0.65 < 0.7, condition is true, so alert is set to 'Bias detected'.

3. Final Answer:

   'Bias detected' -> Option D

4. Quick Check:

   Min fairness < 0.7 means bias alert [OK]

Hint: Check minimum fairness score for bias alert [OK]

Common Mistakes:

• Confusing greater than and less than signs
• Expecting error instead of string output
• Ignoring dictionary value extraction

4. You have this code snippet to log AI model decisions for explainability:

def log_decision(input, decision):
    print(f"Input: {input}, Decision: {decision}")

log_decision('data1', decision)

What is the error in this code?

medium

A. Print statement syntax error

B. Function name is invalid

C. Missing quotes around 'decision' in function call

D. No error, code runs fine

Solution

1. Step 1: Check function call parameters

   The call uses decision without quotes, but decision is not defined as a variable.

2. Step 2: Identify correct usage

   To pass the string 'decision', it must be in quotes: 'decision'.

3. Final Answer:

   Missing quotes around 'decision' in function call -> Option C

4. Quick Check:

   Undefined variable needs quotes [OK]

Hint: Strings need quotes in function calls [OK]

Common Mistakes:

• Assuming variable 'decision' is predefined
• Ignoring syntax of print with f-string
• Thinking function name causes error

5. You want to build a monitoring system that alerts when AI model fairness drops below 0.75 and also logs explanations for decisions. Which combination of practices best supports Responsible AI?

hard

A. Only monitor model speed and ignore fairness

B. Use fairness metrics for alerts and log decision explanations transparently

C. Log decisions but do not monitor fairness scores

D. Monitor fairness but keep decision logic secret

Solution

1. Step 1: Identify key Responsible AI practices

   Responsible AI requires fairness monitoring and transparent explanations.

2. Step 2: Evaluate options for best fit

   Use fairness metrics for alerts and log decision explanations transparently combines fairness alerts and transparent logging, matching Responsible AI goals.

3. Final Answer:

   Use fairness metrics for alerts and log decision explanations transparently -> Option B

4. Quick Check:

   Fairness + transparency = Responsible AI [OK]

Hint: Combine fairness alerts with transparent logs [OK]

Common Mistakes:

• Ignoring fairness monitoring
• Hiding decision explanations
• Focusing only on performance metrics