Year 9 › Data Science and AI › Lesson 5 of 5
Year 9 · Spring 1 · Data Science and AI How machine learning learns, and where bias comes from
Topic 18 Data, spreadsheets and AI
KS3 Computing · Data is raw facts. Information is data with meaning. Spreadsheets calculate, databases store and search, and AI learns patterns from examples.
Managing Data Computational Thinking Cambridge Lower Secondary Computing 0860
You will learn to Explain the difference between a human writing the rules and machine learning, which finds rules from examples. Write a rule that sorts 20 example cards, then test it on cards it has not seen. Explain why the rule changed when the examples changed. Explain how biased training data leads to unfair answers, with an example.
Success criteria I can write a sorting rule from the examples and test it on new cards. I can say what went wrong when our rule met an unusual card. I can give one real example where AI bias could be unfair to people.
Machine learning The computer finds rules itself from labelled examples.
Formula A spreadsheet calculation. Always starts with =.
Function A subroutine that returns a value.
Record / field One row in a database table: everything stored about one thing.
Primary key A field whose value is unique for every record.
SQL Structured Query Language: asks databases questions.
AI and machine learning Normal programming: a human writes the rules.Machine learning : you give thousands of labelled examples ; the computer finds the rules itself.Collect training data. Train: adjust until guesses are mostly right. Test on examples it hasn't seen. Use it on new data. ! Watch outBias: a model learns whatever is in its examples, including unfairness. Unfair data in, unfair decisions out.
e.g. ExampleChatbots predict likely next words. Very useful, but they can be confidently wrong, so check them.
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