Ethics and bias in AI in Intro to Computing - Real World Applications

Real World Mode - Ethics and bias in AI

Ethics and Bias in AI: The Librarian Analogy

Imagine a large public library where a librarian helps visitors find books and information. This librarian represents an AI system. The librarian tries to be helpful and fair, but sometimes the books on the shelves reflect the opinions and history of the people who wrote them. If the librarian only recommends certain books or ignores others, visitors might get a one-sided view. This is like bias in AI. Ethics in AI is like the library's rules to make sure the librarian treats everyone fairly, respects privacy, and provides balanced information.

Mapping AI Ethics and Bias to the Librarian Analogy

Computing Concept	Real-World Equivalent
AI System	The librarian helping visitors find information
Training Data	The collection of books and materials the librarian uses
Bias in AI	The librarian favoring certain books or topics due to the collection's limitations or past choices
Ethical Guidelines	The library rules ensuring fair treatment, privacy, and balanced recommendations
Algorithm Decisions	The librarian's choices on which books to suggest or prioritize
Feedback and Correction	Visitors pointing out missing or unfair recommendations, leading to library updates

A Day in the Library: Understanding AI Ethics and Bias

One day, a visitor asks the librarian for books about local history. The librarian quickly suggests several books but mostly from one perspective because the library's collection is richer in those. Another visitor notices this and tells the librarian about missing viewpoints. The librarian then works with the library team to add more diverse books and update the recommendation approach. This process shows how AI systems can unintentionally be biased and how ethical practices help improve fairness and accuracy over time.

Where the Librarian Analogy Breaks Down

• The librarian is a human with feelings and intentions, while AI is a machine following programmed rules without consciousness.
• The library collection is physical and updated manually, whereas AI training data can be vast and updated through complex automated processes.
• Visitors can directly talk to the librarian, but users may not always understand or influence AI decisions easily.
• The analogy simplifies the technical complexity of algorithms and data processing involved in AI.

Self-Check Question

In our analogy, what would the library rules be equivalent to in AI?

Key Result

Ethics and bias in AI are like a librarian following fair library rules to recommend balanced books without favoritism.

Practice

1. What does ethics in AI mainly focus on?

easy

A. Making fair and safe choices in AI systems

B. Improving AI speed and performance

C. Adding more features to AI software

D. Reducing the cost of AI development

Solution

1. Step 1: Understand the meaning of ethics in AI

   Ethics in AI means making decisions that are fair and safe for everyone.

2. Step 2: Compare options with the definition

   Only Making fair and safe choices in AI systems talks about fairness and safety, which matches ethics in AI.

3. Final Answer:

   Making fair and safe choices in AI systems -> Option A

4. Quick Check:

   Ethics = Fair and safe choices [OK]

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

Common Mistakes:

• Confusing ethics with technical improvements
• Thinking ethics is about cost or speed
• Ignoring fairness and safety aspects

2. Which of the following is the correct way to describe bias in AI?

easy

A. A feature that makes AI faster

B. Unfairness that sneaks into AI from data or design

C. A tool to improve AI accuracy

D. A method to reduce AI errors

Solution

1. Step 1: Define bias in AI context

   Bias means unfairness that can come from the data or how AI is designed.

2. Step 2: Match options with bias definition

   Unfairness that sneaks into AI from data or design correctly describes bias as unfairness sneaking into AI.

3. Final Answer:

   Unfairness that sneaks into AI from data or design -> Option B

4. Quick Check:

   Bias = Unfairness in AI [OK]

Hint: Bias means unfairness in AI data or design [OK]

Common Mistakes:

• Thinking bias improves AI
• Confusing bias with speed or error reduction
• Ignoring the unfairness aspect

3. Consider this simple AI decision flow:

Input: Age
If Age < 18 then Output = "Minor"
Else Output = "Adult"

What is the output if the input age is 20?

medium

A. "Minor"

B. "Unknown"

C. "Error"

D. "Adult"

Solution

1. Step 1: Check the input value against the condition

   Input age is 20, which is not less than 18.

2. Step 2: Follow the decision flow for age 20

   Since 20 ≥ 18, output is "Adult".

3. Final Answer:

   "Adult" -> Option D

4. Quick Check:

   Age 20 ≥ 18 -> Output = "Adult" [OK]

Hint: Check if age is less than 18 or not [OK]

Common Mistakes:

• Choosing "Minor" for age 20
• Assuming error without reason
• Ignoring the else condition

4. An AI system shows bias by unfairly favoring one group. Which fix helps reduce this bias?

medium

A. Use more diverse and balanced data for training

B. Make the AI run faster

C. Add more layers to the AI model

D. Ignore the bias since it is small

Solution

1. Step 1: Identify cause of bias

   Bias often comes from unbalanced or non-diverse training data.

2. Step 2: Choose the fix that addresses data bias

   Using diverse and balanced data helps AI treat all groups fairly.

3. Final Answer:

   Use more diverse and balanced data for training -> Option A

4. Quick Check:

   Diverse data reduces bias [OK]

Hint: Fix bias by improving training data diversity [OK]

Common Mistakes:

• Thinking speed or model size fixes bias
• Ignoring bias problems
• Assuming bias is acceptable if small

5. A company wants to build an AI hiring tool that avoids bias. Which approach best ensures fairness?

hard

A. Use AI to select candidates faster without checks

B. Train AI only on resumes from one city

C. Regularly test AI decisions for fairness and update data

D. Exclude all personal information from resumes

Solution

1. Step 1: Understand fairness in AI hiring

   Fair AI must be tested regularly to catch bias and updated with good data.

2. Step 2: Evaluate options for fairness

   Regularly test AI decisions for fairness and update data includes testing and updating, which helps maintain fairness over time.

3. Step 3: Consider why other options fail

   Train AI only on resumes from one city limits data diversity; C ignores fairness; D may help but alone is not enough.

4. Final Answer:

   Regularly test AI decisions for fairness and update data -> Option C

5. Quick Check:

   Testing and updating ensures fairness [OK]

Hint: Fair AI needs ongoing testing and data updates [OK]

Common Mistakes:

• Using limited data causing bias
• Ignoring fairness checks
• Assuming removing info alone fixes bias