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Define: algorithm
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All Flashcards in Topic 3.2
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3.2.14 cards
Define: algorithm
A set of defined steps that solves a problem or carries out a task.
The four characteristics of an algorithm
Unambiguous, finite, well-defined inputs and outputs, feasible.
How is an algorithm's effectiveness judged?
Often by efficiency — how much work it does as the size of the job grows.
Is an algorithm the same as a program?
No. A recipe is an algorithm. A program is one way of expressing an algorithm so a computer can run it.
3.2.24 cards
The four components of an algorithm
Instructions, variables, conditionals, loops.
What does a conditional do?
Runs a step only if something is true — which is where decisions, and unfairness, enter an algorithm.
What does a loop do?
Repeats steps until a condition is met, applying one rule to every item — which turns a rule into a policy.
Why is a threshold never neutral?
Wherever the line falls, someone sits just the wrong side of it, and the rest of the system cannot see them.
3.2.34 cards
Four ways to represent an algorithm
Natural language, flow chart, pseudocode, and code in a programming language.
Flow chart shapes
A rectangle is a step. A diamond is a decision with two ways out.
Why does the form matter socially?
It decides who can read and therefore check the rule. Code excludes almost everyone; a flow chart excludes almost nobody.
Weakness of natural language
It is ambiguous — the one thing an algorithm must not be.
3.2.44 cards
Four core operations on a set
Sorting (reorder), searching (find one), filtering (remove), classifying (label).
Other uses the guide names
Prioritising, associating, counting, software development, and machine learning.
Sorting vs filtering
Sorting keeps everyone and changes the order. Filtering removes, and the removed are invisible to everything downstream.
The question that opens a counter-argument
What happens to the person at the bottom of the ranking?
3.2.54 cards
The algorithmic dilemmas the guide names
Bias and fairness; accountability; transparency and black boxes; erosion or loss of human judgement.
Where does algorithmic bias come from?
The training data, who is missing from it, stand-in measures, and the target chosen — not from a biased rule.
Why is removing a protected field not enough?
Other fields stand in for it — postcode, school, a gap in work — so the model rebuilds it.
Why is transparency alone not enough?
Knowing why you were rejected is useless if nobody is obliged to reconsider. Accountability is a separate requirement.
Topic 3.2 study notes
Full notes & explanations for Algorithms
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