AI ethics and responsible usage in Prompt Engineering / GenAI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is AI ethics?

AI ethics is a set of moral principles that guide how artificial intelligence should be designed, developed, and used responsibly to avoid harm and ensure fairness.

beginner

Why is fairness important in AI?

Fairness ensures AI systems do not discriminate against people based on race, gender, or other personal traits, promoting equal treatment for everyone.

beginner

What does transparency mean in AI?

Transparency means making AI decisions understandable and clear so users know how and why the AI made a choice.

intermediate

Give an example of responsible AI usage.

Using AI to help doctors diagnose diseases while protecting patient privacy and avoiding biased results is an example of responsible AI usage.

beginner

What is bias in AI and why should it be avoided?

Bias in AI happens when the system unfairly favors or harms certain groups. It should be avoided to ensure AI treats everyone equally and justly.

What does AI ethics primarily focus on?

A Reducing AI size

B Ensuring AI is used fairly and safely

C Increasing AI profits

D Making AI faster

AI ethics focuses on fairness, safety, and responsible use of AI.

Which of the following is a key principle of responsible AI?

A Transparency

B Bias

C Secrecy

D Complexity

Transparency helps users understand AI decisions, a key part of responsible AI.

Why should AI systems avoid bias?

A To confuse users

B To make AI slower

C To increase costs

D To treat all people fairly

Avoiding bias ensures AI treats everyone fairly and equally.

What is an example of responsible AI usage?

A Using AI to spread false information

B Using AI to invade privacy

C Using AI to help doctors while protecting privacy

D Using AI to discriminate

Helping doctors while protecting privacy is responsible AI use.

Transparency in AI means:

A Making AI decisions clear and understandable

B Hiding how AI works

C Making AI secretive

D Making AI decisions random

Transparency means AI decisions are clear and understandable.

Explain why fairness and bias are important considerations in AI ethics.

Think about how AI should treat different people.

Describe what responsible AI usage means and give a real-life example.

Consider how AI can help without causing problems.

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