Ethics and bias in AI in Intro to Computing - Draw & Build Visually

Draw This - beginner

Draw a flowchart that shows the process of identifying and reducing bias in an AI system. Include steps for data collection, bias detection, bias mitigation, and ethical review.

10 minutes

Hint 1

Hint 2

Hint 3

Hint 4

Grading Criteria

Start and End symbols present

Data collection step included

Decision diamond used for bias detection

Separate steps for bias mitigation and ethical review

Flow paths correctly lead from bias detection to mitigation or ethical review

Finalization step present after ethical review

Solution

  +-------------------+
  |   Start           |
  +---------+---------+
            |
            v
  +-------------------+
  | Collect Data      |
  +---------+---------+
            |
            v
  +-------------------+
  | Detect Bias?      |
  +----+---------+----+
       |         |
      No        Yes
       |         |
       v         v
  +-------------------+    +-------------------+
  | Ethical Review    |    | Mitigate Bias     |
  +---------+---------+    +---------+---------+
            |                        |
            v                        v
  +-------------------+    +-------------------+
  | Finalize AI System |<---+ Ethical Review    |
  +---------+---------+    +-------------------+
            |
            v
  +-------------------+
  |       End         |
  +-------------------+

This flowchart starts with collecting data, which is the first step in building an AI system. Then it checks if there is any bias detected in the data or AI behavior using a decision diamond labeled 'Detect Bias?'. If no bias is found, the process moves directly to an ethical review step to ensure the AI system meets ethical standards before finalizing.

If bias is detected, the flowchart directs to a 'Mitigate Bias' step where actions are taken to reduce or remove bias. After mitigation, the AI system undergoes another ethical review to confirm the bias has been addressed properly. Once ethical review is passed, the AI system is finalized and the process ends.

This step-by-step process helps ensure AI systems are fair, ethical, and less biased.

Variations - 2 Challenges

[intermediate] Draw a flowchart for identifying bias in AI that includes feedback from users and continuous monitoring after deployment.

[advanced] Draw a detailed flowchart showing how an AI system handles multiple types of bias (e.g., gender, race, age) with separate mitigation strategies for each.

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