Why Ethics and bias in AI in Intro to Computing? - Purpose & Use Cases

The Big Idea

What if a computer decides your future unfairly without you knowing?

The Scenario

Imagine a company using a computer program to decide who gets a loan. If the program is unfair, some people might be wrongly denied money just because of their background or where they live.

The Problem

Without thinking about ethics and bias, these programs can make mistakes that hurt real people. It's like using a broken scale that always weighs some people heavier than others, leading to unfair results.

The Solution

By learning about ethics and bias in AI, we can build fairer programs. This means checking the data and rules carefully so everyone gets a fair chance, just like making sure a scale is balanced before weighing.

Before vs After

✗ Before

if applicant_location == 'X': deny_loan()

✓ After

if not biased_data(applicant): evaluate_loan(applicant)

What It Enables

Understanding ethics and bias in AI helps create technology that treats everyone fairly and builds trust in computer decisions.

Real Life Example

When AI helps doctors decide treatments, ethical AI ensures patients get care based on their health, not on unfair factors like race or gender.

Key Takeaways

Ignoring ethics can cause unfair, harmful decisions.

Bias in data leads to wrong results in AI programs.

Learning ethics helps build fair and trustworthy AI.

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