AI ethics and responsible usage in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a principle that AI should respect human rights.

Prompt Engineering / GenAI

ai_principle = '[1]'

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

ARespect for human rights

BSpeed

CEfficiency

DFairness

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing technical terms like 'Efficiency' instead of ethical principles.

Confusing speed or performance with ethical responsibility.

The correct principle is 'Respect for human rights' because AI systems must protect and uphold human dignity and rights.

2fill in blank

medium

Complete the code to identify a key risk of AI misuse.

Prompt Engineering / GenAI

risk = '[1]'

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

AFaster computation

BBetter data storage

CBias and discrimination

DImproved graphics

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting technical improvements instead of ethical risks.

Confusing performance benefits with risks.

Bias and discrimination are major risks when AI systems learn from unfair or incomplete data, leading to unfair treatment of people.

3fill in blank

hard

Fix the error in the code to ensure AI transparency is correctly stated.

Prompt Engineering / GenAI

ai_principle = '[1]'

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

ATransparency

BOpacity

CSecrecy

DConfusion

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing words that mean hiding information.

Confusing transparency with technical terms.

Transparency means AI systems should be clear and understandable, which is important for trust and accountability.

4fill in blank

hard

Fill both blanks to create a dictionary that maps AI ethical principles to their descriptions.

Prompt Engineering / GenAI

ai_ethics = { '[1]': 'AI should be fair and unbiased', '[2]': 'AI decisions should be explainable' }

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

AFairness

BTransparency

CPrivacy

DSpeed

Attempts:

3 left

💡 Hint

Common Mistakes

Using technical terms like 'Speed' instead of ethical principles.

Mixing up privacy with transparency.

'Fairness' relates to unbiased AI, and 'Transparency' relates to explainable AI decisions.

5fill in blank

hard

Fill all three blanks to create a list of AI responsible usage practices.

Prompt Engineering / GenAI

responsible_usage = ['[1]', '[2]', '[3]']

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

AData privacy

BRegular audits

CUser consent

DFaster processing

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing technical improvements like 'Faster processing' instead of ethical practices.

Ignoring user rights and oversight.

Responsible AI use includes protecting data privacy, performing regular audits to check fairness, and obtaining user consent.

Practice

1. What is the main goal of AI ethics?

easy

A. To increase AI's data storage

B. To make AI run faster

C. To reduce AI's power consumption

D. To make sure AI is fair, safe, and respects people

Solution

1. Step 1: Understand AI ethics purpose

   AI ethics focuses on fairness, safety, and respect for people when using AI.

2. Step 2: Compare options to this purpose

   Only To make sure AI is fair, safe, and respects people matches this goal; others focus on technical aspects unrelated to ethics.

3. Final Answer:

   To make sure AI is fair, safe, and respects people -> Option D

4. Quick Check:

   AI ethics = fairness and safety [OK]

Hint: Ethics means fairness and safety in AI [OK]

Common Mistakes:

• Confusing ethics with technical performance
• Thinking ethics is about speed or storage
• Ignoring fairness and respect aspects

2. Which of the following is a correct practice to protect user privacy in AI?

easy

A. Collect all user data without consent

B. Share user data publicly for transparency

C. Use data anonymization before training AI

D. Ignore data protection laws

Solution

1. Step 1: Identify privacy protection methods

   Data anonymization removes personal details to protect privacy.

2. Step 2: Evaluate options for privacy respect

   Only Use data anonymization before training AI uses anonymization; others violate privacy or laws.

3. Final Answer:

   Use data anonymization before training AI -> Option C

4. Quick Check:

   Privacy protection = anonymize data [OK]

Hint: Anonymize data to protect privacy [OK]

Common Mistakes:

• Assuming collecting all data is okay
• Confusing transparency with sharing private data
• Ignoring legal rules on data

3. Consider this code snippet that checks for bias in AI predictions:

predictions = ["male", "female", "male", "male", "female"]
if predictions.count("female") / len(predictions) < 0.3:
    print("Bias detected")
else:
    print("No bias")

What will this code print?

medium

A. Bias detected

B. No bias

C. Error: division by zero

D. Error: count method not found

Solution

1. Step 1: Calculate female ratio in predictions

   Count of "female" is 2, total predictions are 5, ratio = 2/5 = 0.4.

2. Step 2: Compare ratio to 0.3 threshold

   0.4 is not less than 0.3, so else branch runs printing "No bias".

3. Final Answer:

   No bias -> Option B

4. Quick Check:

   Female ratio 0.4 > 0.3 means no bias [OK]

Hint: Calculate ratio and compare to threshold [OK]

Common Mistakes:

• Miscounting female occurrences
• Confusing < with > in condition
• Assuming code errors without checking

4. This code aims to log AI decisions for transparency but has an error:

decisions = ["approve", "deny", "approve"]
for i in range(len(decisions))
    print(f"Decision {i}: {decisions[i]}")

What is the error and how to fix it?

medium

A. Missing colon after for loop; add ':' at end of for line

B. Wrong variable name; change 'i' to 'index'

C. Print statement syntax error; remove f-string

D. decisions list is empty; add elements

Solution

1. Step 1: Identify syntax error in for loop

   The for loop line lacks a colon at the end, causing a syntax error.

2. Step 2: Fix syntax by adding colon

   Add ':' after 'range(len(decisions))' to correct the loop syntax.

3. Final Answer:

   Missing colon after for loop; add ':' at end of for line -> Option A

4. Quick Check:

   For loop needs ':' [OK]

Hint: Check for missing colons in loops [OK]

Common Mistakes:

• Changing variable names unnecessarily
• Removing valid f-string formatting
• Assuming list is empty without checking

5. You want to build an AI system that recommends jobs fairly to all genders. Which approach best ensures ethical and responsible usage?

hard

A. Train on balanced data, anonymize gender info, and explain recommendations

B. Use only male data to improve accuracy

C. Ignore fairness to speed up training

D. Share all user data publicly for transparency

Solution

1. Step 1: Identify ethical practices for fairness

   Balanced data avoids bias; anonymizing protects privacy; explanations build trust.

2. Step 2: Evaluate options for responsible AI

   Only Train on balanced data, anonymize gender info, and explain recommendations combines fairness, privacy, and transparency correctly.

3. Final Answer:

   Train on balanced data, anonymize gender info, and explain recommendations -> Option A

4. Quick Check:

   Fairness + privacy + transparency = Train on balanced data, anonymize gender info, and explain recommendations [OK]

Hint: Balance data, protect privacy, explain AI decisions [OK]

Common Mistakes:

• Using biased data sets
• Ignoring privacy laws
• Confusing transparency with sharing private data