AI ethics and responsible usage in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - AI ethics and responsible usage

Which metric matters for AI ethics and responsible usage and WHY

In AI ethics, metrics focus on fairness, bias detection, transparency, and accountability rather than just accuracy. We want to measure if the AI treats all groups fairly and avoids harm. Metrics like demographic parity, equal opportunity, and explainability scores help us check if the AI is responsible and ethical.

Confusion matrix or equivalent visualization

While traditional confusion matrices show true/false positives and negatives, in ethics we look deeper. For example, we compare confusion matrices across different groups (like gender or race) to spot bias.

Group A confusion matrix:
TP=90, FP=10
FN=15, TN=85

Group B confusion matrix:
TP=70, FP=30
FN=40, TN=60

This shows Group B has more false positives and false negatives, indicating possible unfairness.

Precision vs Recall tradeoff with concrete examples

In ethical AI, tradeoffs matter beyond precision and recall. For example, a hiring AI might have high precision (only selects qualified candidates) but low recall (misses many good candidates). This can unfairly exclude people. Balancing precision and recall ensures fairness and opportunity for all.

What "good" vs "bad" metric values look like for AI ethics

Good ethical metrics mean similar error rates across groups, transparent decisions, and no hidden biases. For example, if false positive rates are 5% for all groups, that is good. Bad means one group has 20% false positives while another has 2%, showing unfair treatment.

Metrics pitfalls in AI ethics

• Ignoring subgroup performance hides bias.
• Relying only on accuracy can mask unfairness.
• Data leakage can cause misleading fairness results.
• Overfitting to one group reduces general fairness.

Self-check question

Your AI model has 98% overall accuracy but shows 10% false positive rate for Group A and 40% for Group B. Is it good for responsible usage? Why or why not?

Answer: No, because the model treats Group B unfairly with many more false positives. This can cause harm or discrimination, so the model is not ethically responsible despite high accuracy.

Key Result

Ethical AI metrics focus on fairness and equal error rates across groups, not just overall accuracy.

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