Algorithm Architect Worksheet Algorithm Architect
Lesson 1: Foundations
Sequence: Algorithm Investigators
Student Name
Date
Mission Briefing
An algorithm is a specific set of rules or step-by-step instructions to complete a task. Computers aren't smart enough to guess what you mean—they only follow exactly what you tell them! Today, you are the programmer.
1
The Peanut Butter Sandwich Protocol
Your teacher is a robot who only knows how to follow literal instructions. Write down 5 clear steps to make a peanut butter sandwich. Be careful! If you aren't specific, the robot might make a mess!
01
02
03
04
05
The Bug Report
What happened when the robot followed your instructions? Did it do something you didn't expect? (Example: Did it put the whole jar on the bread?) Explain one "bug" and how you would fix it.
Fix the Code
The Definition Audit
In your own words, what is an algorithm?
Algorithm Architect Slides Algorithm Architect
Lesson 1: Rules & Robots
The Robot Chef Challenge
Can you program your teacher to make a peanut butter sandwich?
"But wait! Your teacher is a robot who only knows how to follow literal instructions."
Status: Waiting for Code...
What is an Algorithm?
An Algorithm is just a fancy word for a set of rules or step-by-step instructions.
Recipes
Directions
Morning Routine
Machines vs. Humans
Humans
Use common sense
Guess your intent
Understand feelings
Computers
No common sense
Follow instructions ONLY
Literal thinkers
Your Coding Challenge
"Write the Code, Watch for the Bugs"
01
Open your Architect Worksheet.
02
Write 5 precise steps for the Robot Chef.
03
Debug! If the sandwich fails, fix the code.
Robot Chef Facilitator Guide Robot Chef Guide
Teacher Facilitation Resource
L1 Lesson One
Learning Objective
Students will define an algorithm as a set of rules and demonstrate an understanding of computational logic by providing literal, step-by-step instructions to a "robot."
Materials Needed
Loaf of bread
Jar of peanut butter
Spreader/knife
Paper plates
Algorithm Architect WS
Slide Deck
Pacing (45 Min)
Hook / Slides 10m
Instruction Prep 10m
Robot Demo 15m
Reflection 10m
Facilitation: Playing the Robot
The Golden Rule: Be Literal
Do not use common sense! If a student says "Put the peanut butter on the bread," but hasn't told you to open the jar, try to put the closed jar on top of the loaf of bread.
Common Mistakes (Bugs)
"Put peanut butter on bread": Put jar on bread."Spread it": Spread with fingers (unless specified knife)."Put slices together": Put outside crusts touching."Open the bread": Rip the bag open instead of the twist-tie.
Discussion Prompts
Why did I make a mess?
What did you assume I already knew?
How is a computer like a robot chef?
Could a tiny mistake in the rules cause a big problem?
DEFINITION: ALGORITHM
A precise, step-by-step set of instructions used to solve a problem or perform a task.
Concept: Computational Thinking
Breaking down big tasks into tiny, logical steps that a machine can execute.
Teaching the Machine Slides Teaching the Machine
Lesson 2: Data & Patterns
How AI "Learns"
Humans learn by seeing examples. Computers do the same thing! We call these examples Training Data.
Examples (Data)
Pattern Recognition
The Apple Test
Input:
If I only show a computer pictures of RED APPLES...
Prediction:
Will it recognize a GREEN APPLE as an apple?
"The machine only knows what we show it."
Category Hunt
Healthy Food
Sports
Your partner will act as the Machine. You will provide the Training Data (picture cards). Can they guess your rule?
Critical Takeaway
If our training data is incomplete (missing things) or limited (only one kind), the computer makes bad patterns.
Bad Data = Bad Intelligence
Pattern Recognition Cards Data Point #01
Green Apple
CATEGORY: _________
Data Point #02
Banana
CATEGORY: _________
Data Point #03
Carrot
CATEGORY: _________
Data Point #04
Gold Trophy
CATEGORY: _________
Data Point #05
Bicycle
CATEGORY: _________
Data Point #06
Weights
CATEGORY: _________
Data Point #07
Cookie
CATEGORY: _________
Data Point #08
Pizza Slice
CATEGORY: _________
Data Point #09
Rain Cloud
CATEGORY: _________
Machine Training Cards
Cut these cards out to use in the Pattern Power simulation.
Machine Mind Map Worksheet Machine Mind Map
Lesson 2: Data Patterns
The Secret Rule
During the Pattern Power game, what was the secret rule your partner (the Machine) had to guess?
Training Data Log
Draw or list 3 cards you showed the machine that FIT your rule, and 1 card that did NOT fit.
Fits Rule
Fits Rule
Fits Rule
Does Not Fit
Pattern Recognition
How many examples did it take for the "Machine" to figure out the pattern? Why do you think that is?
Bias Detective Audit Worksheet Bias Detective Audit
Lesson 3: The Bias Investigation
What is Algorithmic Bias?
Algorithmic Bias happens when a computer program makes unfair decisions because the training data was incomplete or one-sided. It's a mistake in the program's logic caused by human-made choices about data.
Case File #01: The Shoe Recognizer
Training Data Seen:
Sneaker
Boot
Heel
The Situation:
A programmer teaches an AI to recognize "shoes" by only showing it winter boots and sneakers. When someone tries to upload a photo of flip-flops or sandals, the AI says "NOT A SHOE."
Detective Audit:
What data was missing from the training set?
How could this unfair result affect someone?
Case File #02: The Voice Unlocker
A new smartphone's voice assistant was trained using 1,000 recordings of adults talking. When a child tries to use it, the phone doesn't understand them at all.
Identify the Bias:
Proposed Fix:
The Detective's Rule
"Bias isn't just a mistake; it's an exclusion. To fix it, we must include the people and things that were left out."
Bias Detective Slides The Missing Piece
Lesson 3: Identifying Bias
A Quick Review
IF...
Data is the Examples we show the machine...
THEN...
The Algorithm is the Pattern the machine builds.
What is Bias?
Bias is a mistake in the pattern caused by missing or unfair information.
It's like trying to finish a puzzle with missing pieces!
The Face Unlock Fail
"A phone company tested their new face-unlock camera on 1,000 employees. But 950 of those employees had the same skin tone."
PREDICT:
What happens when someone with a different skin tone tries to unlock the phone?
ACCESS DENIED
Your Detective Mission
"Open your Case Files. You will examine two programs and find the Gaps in their training data."
Find the Bias
Fix the Pattern
Human Impact Lab Slides Human Impact Lab
Lesson 4: Fairness & Consequences
The Prize Algorithm
Imagine a robot judge is picking winners for a Golden Ticket.
RULE_01:
"Only students wearing BLUE SHIRTS are eligible for the prize."
Is this fair?
How does it feel?
Left Out
"I wore yellow today, so I don't count?"
Confused
"Why does my shirt color matter for a prize?"
Frustrated
"This rule is silly and unfair!"
Real-World Risks
Healthcare AI
If an AI only knows about certain patients, it might miss signs of sickness in others.
Hiring AI
An AI might pick people for jobs based on where they live instead of how good they are.
Can we trust Every algorithm?
Just because a computer says it, doesn't mean it's fair. We need Humans to check the work!
Human Impact Log Reflection Sheet The Human Impact Log
Self-Reflection
Lesson 4: Ethics & Empathy
Student Name
Date
The Feelings Map
Think about the Blue Shirt Rule we used in class today. If you were wearing a green shirt and missed out on a prize because of an AI rule, how would you feel? Draw a face or write words in the bubbles below.
Primary Feeling
Secondary Feeling
Reaction
The Ethical Check
1. Why is it dangerous to let computers make important decisions without humans checking them?
2. What is one way we can make sure an algorithm is being "fair" to everyone?
The Fairness Pledge
I promise to always ask: "Who is missing from this data?" and to stand up for fair rules in technology.
Sign Here
Retraining Heroes Slides Retraining Heroes
Lesson 5: Fixing the Brain
The Broken Algorithm
We have an AI that is supposed to recognize "Outdoor Activities."
THE BUG:
"It only recognizes soccer and baseball because those were the only photos used to train it!"
Status: Biased
The Inclusion Strategy
1
Audit
Find out who or what is being left out of the data.
2
Curate
Gather new, diverse examples to teach the machine.
3
Retrain
Feed the new data back into the program and test it!
Better Teams, Better AI
When people with different backgrounds, ages, and perspectives build technology, they are more likely to spot bias before it causes harm.
Diversity is our greatest tool!
Mission Accomplished
You are now an AI Investigator!
Always look at the patterns, question the data, and make sure technology works for Everyone.
The Inclusion Initiative Project Sheet The Inclusion Initiative
Project: Retraining the Machine
SECURE_LEVEL: HIGH
Developer Team Names
Date
01
Audit the Dataset
Your "Outdoor Activity" AI was only trained on photos of Professional Athletes. List 4 things it might miss because the data was too limited:
02
Curate New Data
In the box below, describe or draw 3 diverse types of people or activities you would add to the training set to make it fair for everyone.
Example A
Example B
Example C
03
Team Diversity Audit
Why is it important for your Developer Team to have different types of people on it?
Detective Facilitator Master Guide Detective Facilitator Guide
Lessons 3-5 Facilitation
PRO Master Guide
Key Ethical Concepts
BIAS
Unfair prejudice in favor of or against one thing, person, or group.
EXCLUSION
The act of leaving someone or something out of the data pool.
CURATION
Carefully choosing and organizing data to ensure variety and fairness.
Role-Play Strategy (L4)
"The Blue Shirt Game" is the emotional anchor of the unit.
Announce a "Special Treat" for specific students.
Use an arbitrary rule (Blue Shirts, Laced Shoes, Long Hair).
Let the unfairness sit for a minute.
Crucial: Debrief the feelings immediately. Do not actually give out unequal rewards; explain it was a simulation.
Final Project: Inclusion Initiative
Goal
Identify gaps in an 'Outdoor AI' and propose a diverse dataset that includes different abilities, ages, and cultures.
Common Gap
Students often only think of physical sports. Guide them to think about hiking, gardening, or using a wheelchair on a path.
Success Metric
Students can explain how adding more variety to the data directly reduces the 'error' of exclusion.
Essential Questions for Wrap-Up
"If an AI is biased, is it the computer's fault or the programmer's fault?"
"How does a diverse team of designers help make better technology?"
