AI governance frameworks in Prompt Engineering / GenAI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Key Principle of AI Governance Frameworks

Which of the following is a fundamental principle commonly emphasized in AI governance frameworks to ensure responsible AI use?

A Ignoring data privacy to improve AI accuracy

B Ensuring transparency and explainability of AI decisions

C Prioritizing AI development over human rights

D Maximizing AI system speed regardless of ethical concerns

💡 Hint

Think about what helps users understand AI decisions.

Transparency and explainability help users and stakeholders understand how AI systems make decisions, which is key for trust and accountability.

❓ Model Choice

intermediate

Choosing a Model for Fairness Evaluation

You want to evaluate if an AI model treats different demographic groups fairly. Which approach aligns best with AI governance frameworks focused on fairness?

A Compare model performance metrics separately for each demographic group

B Use a single accuracy metric on the whole dataset

C Ignore demographic data to avoid bias

D Only test the model on the majority group data

💡 Hint

Fairness means checking if all groups are treated equally.

Evaluating performance separately for each group helps detect biases and ensures fairness across demographics.

❓ Metrics

advanced

Interpreting Bias Metrics in AI Governance

An AI governance team uses disparate impact ratio to assess bias. If the ratio is 0.6 for a protected group, what does this indicate?

A The protected group is favored by the AI model

B The ratio indicates no bias

C The AI model has perfect fairness

D The protected group is disadvantaged compared to the reference group

💡 Hint

Disparate impact less than 0.8 usually signals bias.

A ratio below 0.8 means the protected group receives less favorable outcomes, indicating bias.

🔧 Debug

advanced

Identifying Governance Issue in AI Deployment Code

Consider this pseudocode snippet for deploying an AI model: model = train_model(data) if model.accuracy > 0.9: deploy(model) else: deploy(model) What governance issue does this code illustrate?

A It deploys the model regardless of accuracy, risking poor performance

B It correctly prevents deployment of low accuracy models

C It ensures fairness by checking demographic metrics

D It uses explainability checks before deployment

💡 Hint

Look at the conditions for deployment.

The code deploys the model no matter the accuracy, which can cause harm if the model is bad.

🧠 Conceptual

expert

Balancing Innovation and Regulation in AI Governance

Which statement best captures the challenge AI governance frameworks face when balancing innovation and regulation?

A Strict regulations always speed up AI innovation

B No regulation is needed if AI is innovative

C Governance must protect society without stifling beneficial AI advances

D Innovation should be stopped to ensure full safety

💡 Hint

Think about how rules can both help and slow progress.

Effective governance balances protecting people and allowing AI to improve lives.

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