AI governance frameworks in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a basic AI governance principle.

Prompt Engineering / GenAI

ai_governance = {"principle": "[1]"}

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

Atransparency

Baccuracy

Cspeed

Dcomplexity

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'accuracy' which is important but not a governance principle here.

The key principle in AI governance is transparency, which means making AI decisions clear and understandable.

2fill in blank

medium

Complete the code to add a governance framework component that ensures fairness.

Prompt Engineering / GenAI

framework = {"fairness": [1]

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

A"fast processing"

B"user interface"

C"data storage"

D"bias detection"

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing unrelated components like 'fast processing'.

Fairness in AI governance involves bias detection to avoid unfair treatment.

3fill in blank

hard

Fix the error in the code to correctly represent an AI governance policy.

Prompt Engineering / GenAI

policy = {"accountability": [1]

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

A"True"

BTRUE

CTrue

Dtrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using quoted "True" which is a string, not a boolean.

In Python dictionaries, boolean values should be capitalized without quotes. Here, True is correct as a boolean value.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters AI risks above a threshold.

Prompt Engineering / GenAI

risks_filtered = {risk: level for risk, level in risks.items() if level [1] [2]

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

A>

B5

C<

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' which would filter lower risks.

The comprehension filters risks with levels greater than 5, so level > 5 is correct.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps AI principles to their descriptions if the description length is greater than 10.

Prompt Engineering / GenAI

principles_desc = [1]: [2] for [3], [2] in principles.items() if len([2]) > 10}

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

Aprinciple

Bdescription

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing variable names or using the same name for key and value.

The comprehension uses principle as key and description as value, iterating over principle, description pairs.

Practice

1. What is the main purpose of an AI governance framework?

easy

A. To increase the speed of AI model training

B. To improve the graphical user interface of AI apps

C. To reduce the cost of AI hardware

D. To guide safe and fair use of AI systems

Solution

1. Step 1: Understand the role of AI governance frameworks

   AI governance frameworks are designed to ensure AI is used responsibly and ethically.

2. Step 2: Identify the main goal

   The main goal is to guide safe and fair use, preventing harm and building trust.

3. Final Answer:

   To guide safe and fair use of AI systems -> Option D

4. Quick Check:

   Purpose of AI governance = safe and fair use [OK]

Hint: Focus on safety and fairness in AI use [OK]

Common Mistakes:

• Confusing governance with technical optimization
• Thinking governance is about cost or speed
• Ignoring ethical and safety aspects

2. Which of the following is a correct component of an AI governance framework?

easy

A. Faster GPU hardware

B. Policies and processes for AI use

C. New programming languages

D. User interface design templates

Solution

1. Step 1: Recall components of AI governance frameworks

   They include principles, policies, processes, roles, and tools to manage AI responsibly.

2. Step 2: Match options to components

   Only policies and processes relate directly to governance frameworks.

3. Final Answer:

   Policies and processes for AI use -> Option B

4. Quick Check:

   Governance components = policies and processes [OK]

Hint: Look for management and rules, not tech specs [OK]

Common Mistakes:

• Choosing hardware or software unrelated to governance
• Confusing governance with development tools
• Ignoring the role of policies

3. Consider this code snippet representing a simple AI governance check in Python:

def check_fairness(data):
    if 'bias' in data:
        return 'Unfair AI detected'
    else:
        return 'AI is fair'

result = check_fairness(['accuracy', 'bias'])
print(result)

What will be the output?

medium

A. Unfair AI detected

B. Error: 'bias' not defined

C. AI is fair

D. None

Solution

1. Step 1: Analyze the function logic

   The function checks if the string 'bias' is in the input list. If yes, it returns 'Unfair AI detected'.

2. Step 2: Check the input data

   The input list contains 'accuracy' and 'bias', so 'bias' is present.

3. Final Answer:

   Unfair AI detected -> Option A

4. Quick Check:

   Presence of 'bias' triggers unfair AI message [OK]

Hint: Check if 'bias' is in the list to decide output [OK]

Common Mistakes:

• Assuming 'bias' is a variable, not a string
• Ignoring the if condition logic
• Thinking the function returns None

4. The following code is intended to check if an AI model meets governance standards by verifying if it has a 'transparency' attribute. Identify the error:

class AIModel:
    def __init__(self, transparency):
        self.transparency = transparency

def check_governance(model):
    if model.transparency == True:
        return 'Governance passed'
    else:
        return 'Governance failed'

model = AIModel('yes')
print(check_governance(model))

medium

A. The transparency attribute should be a boolean, not a string

B. The method check_governance is missing a return statement

C. The class AIModel lacks a constructor

D. The print statement is outside the function

Solution

1. Step 1: Understand the attribute type check

   The function compares model.transparency to True (boolean).

2. Step 2: Check the attribute value in the instance

   The model is created with 'yes' (string), not True (boolean), so the condition fails.

3. Final Answer:

   The transparency attribute should be a boolean, not a string -> Option A

4. Quick Check:

   Type mismatch causes governance check failure [OK]

Hint: Match attribute types with condition checks [OK]

Common Mistakes:

• Ignoring type mismatch between string and boolean
• Thinking missing return causes error here
• Confusing class constructor presence

5. You are designing an AI governance framework for a healthcare AI system. Which combination of components best ensures ethical use and accountability?

hard

A. High accuracy metrics, cloud deployment, and automated updates

B. Faster model training, open-source code, and user-friendly UI

C. Clear policies, regular audits, and defined roles for oversight

D. Large datasets, complex algorithms, and minimal documentation

Solution

1. Step 1: Identify key governance needs in healthcare AI

   Ethical use and accountability require clear rules, monitoring, and responsible roles.

2. Step 2: Evaluate options for governance components

   Only Clear policies, regular audits, and defined roles for oversight includes policies, audits, and roles which align with governance goals.

3. Final Answer:

   Clear policies, regular audits, and defined roles for oversight -> Option C

4. Quick Check:

   Governance needs policies + audits + roles [OK]

Hint: Governance = policies + audits + roles, not tech features [OK]

Common Mistakes:

• Confusing governance with technical performance
• Ignoring the need for oversight roles
• Choosing options focused on speed or complexity