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Intro to CS 9-10 / Week 10 / Friday
5/6
Week 10 · Data, Compression and Models

Friday

Impact Friday: What data reveals
// Shrinking photos, cleaning logs and simulating growth
⏱ about 20 min

Friday: Impact Friday: What Data Reveals

Wren scrolls through a week of Beacon photos to check the lettuce. Then he stops.

"What do you notice?" he asks. "Comet is in the corner of every photo taken after dinner."

Comet leans in. "That is my snack break. I water the basil on the way."

"Beacon was never told to watch the crew," Wren says. "But the camera saw you anyway, and every photo has a time on it."

Nova hovers above the screen. "Put the photos and the times together," she says quietly. "What could someone learn?"

Comet blinks. "My whole evening routine. We need to fix that before launch."

Collected without noticing

Data can be collected from many people, even when they are not using a device or are not near it.

This kind of automatic, hidden collection can raise privacy concerns.

Scattered personal data, such as location, cookies and browsing history, can be combined to learn about a person.

Beacon photos plus their time metadata could reveal when Comet is in the greenhouse.

SPOT THE PRIVACY PROBLEM
  • Read the question.
  • Tap your answer.
What did Beacon collect that nobody planned?
Why is combining photos with their times a concern?
Which fix protects the crew best?

More data does not fix bias

Bias in data is often created by the type or the source of the data that was collected.

Collecting more of the same data does not remove that bias.

Suppose Beacon only measures the sunny beds. A thousand more sunny readings still say nothing about the shady corner.

StatementTrue or false?
Bias often comes from the type or source of the data.?
Collecting more of the same data removes bias.?
Data can be collected from people who are not using a device.?
Combining scattered data can reveal facts about a person.?
Metadata never matter for privacy.?
WHY THIS EXERCISEA trustworthy app thinks about bias and privacy before it collects anything.

Week review

OUR WEEK, IN ORDER
  • ?We checked what Beacon data could reveal about the crew.
  • ?We compressed a lettuce photo with a run-length code.
  • ?We simulated tomato growth with RANDOM.
  • ?We learned why repeated data compresses well.
  • ?We cleaned the Beacon log and met metadata.
WHY THIS EXERCISEThe week moved from shrinking data, to trusting it, to protecting people in it.
LOSSLESS OR LOSSY?
  • Read the question.
  • Tap your answer.
The plant team must count every leaf exactly. Which compression fits?
The relay is very slow and the team only needs a quick look. Which fits?
Making data uniform without changing its meaning is called ____ data.
An abstraction of a complex process built for a purpose is a ____.
MORE DATA, SAME BIAS?
  • Read the question.
  • Tap your answer.
Beacon only measures the sunny beds. Will a thousand more sunny readings fix the bias?
Try it
Think of one device at home that might collect data on its own, such as a thermostat or a doorbell camera.
With a grown-up, talk about what it might collect without anyone noticing.
On paper, sketch a Beacon camera plan from above: the greenhouse beds, the camera, and the area it should see. Shade the parts where crew members walk.

Excellent week, developer. Tomorrow's Mission Quest is a family photo squeeze.

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