A4 AI in Practice

How do models predict, and who is responsible when they are wrong?

Stage: Coders, ages 11 to 14
Lessons: 7
You make: A model with a test report and a short reasoned argument on an AI issue

Before you start. You need Python for these lessons. Ask a grown-up to help you install it from python.org, which comes with a simple editor called IDLE. Do not make accounts or type your name into any website to run code.

These lessons use plain Python that you type in yourself. Every program on this page has been run and checked.

Safety rules · Teacher and parent version of this course

Your lessons

Open a lesson. Tick the boxes as you go. Do one lesson at a time.

Lesson 1: How Models PredictAbout 45 minutes

Get ready
Do it
Check yourself
Show a grown-up

Get ready

You need:

Python and IDLE (or another editor you already use)
Paper

Do it, step by step

Features are the facts a model looks at, such as height and weight. A label is the answer, such as “cat” or “dog”. A prediction is the model’s guess.

Open Python. Type this program and run it. It guesses cat or dog from weight alone, using a dividing line at 8 kg.

# Each pet: [height in cm, weight in kg, what it is]
pets = [
    [25, 4, "cat"], [23, 3, "cat"], [28, 5, "cat"], [30, 6, "cat"],
    [45, 18, "dog"], [55, 25, "dog"], [38, 9, "dog"], [60, 30, "dog"],
]

def predict(weight):
    if weight < 8:      # our dividing line
        return "cat"
    return "dog"

wrong = 0
for height, weight, label in pets:
    guess = predict(weight)
    print(label, "-> machine says", guess)
    if guess != label:
        wrong = wrong + 1

print("Wrong guesses:", wrong)

You should see something like:

cat -> machine says cat
cat -> machine says cat
cat -> machine says cat
cat -> machine says cat
dog -> machine says dog
dog -> machine says dog
dog -> machine says dog
dog -> machine says dog
Wrong guesses: 0

Draw the pets on paper as dots: height across, weight up. Colour cats one colour and dogs another. Draw the dividing line.
Change weight < 8 to weight < 10 and run it again. You should see Wrong guesses: 1. Which pet is wrong now? Why?
Try to find a line that gets every pet right. Is there always a perfect line for any data?

Real AI test (with a grown-up): Ask it to explain your model

Optional, and a grown-up runs it. Under 13, the grown-up types and reads, and you direct and check. Read the real-AI guide first. About 20 minutes.

Why we do this. You wrote a dividing line. See whether an AI explains it correctly. You are the expert, so check it.

Prompt 1: Explain features and labels

Here is a dataset of pets as [height in cm, weight in kg, label]: [25,4,cat], [23,3,cat], [28,5,cat], [30,6,cat], [45,18,dog], [55,25,dog], [38,9,dog], [60,30,dog]. A program says “cat” if the weight is below 10, and “dog” otherwise. Which pets does it get wrong? Explain what the features, the label and the prediction are.

Change only made-up parts in [brackets]. Never add a name, school, address, photo or anything private.

What to look for

You found that a 9 kg dog is wrong when the line is 10. Did it find the same pet?
Are its definitions of feature, label and prediction right?
Did it make any mistake? If so, what was it?

Write it down

Tool, prompt, what it said, what you checked, and whether it was right, wrong or not sure. Record sheet

Think about it. Why do you still need to check an AI’s explanation, even when it sounds right?

Check yourself

What are the features, label and prediction in this lesson?

Open the answer

Features: height and weight. Label: cat or dog. Prediction: the guess the program makes.

Which pet is wrong when the line is 10?

Open the answer

The 9 kg dog (38 cm tall). It falls below the line, so the program says cat.