An algorithm is a set of defined steps that solves a problem or does a task. A recipe is one. So is the rule a bank uses to decide a loan. Four things have to be true, and a question asking for “characteristics” wants these words.
The four characteristics
Unambiguous
Every step means exactly one thing. “Add a little salt” is not a step, because a machine has no judgement to fill the gap.
Finite
It stops. “Stir until it is stirred enough” never ends, and a program that never ends has hung.
Well-defined inputs and outputs
You know what goes in and what comes out, so the result can be checked and the algorithm reused.
Feasible
Every step can actually be carried out with the resources available. Correct but impossible is still broken.
Clear · ends · in and out · doable
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Two algorithms can both be correct and one can be useless. The guide says effectiveness is often judged by efficiency, which means how much work it does as the job gets bigger.
| Finding one name in a list | 10 names | 10 million names |
|---|---|---|
| Check every entry in turn | 10 checks | 10 million checks |
| Halve a sorted list each time | about 4 checks | about 24 checks |
Why this matters here: Efficiency is why the same idea can be impossible on one scale and instant on another. It is also why sorting people became cheap enough to do to everyone, all the time.
One recipe, with each characteristic broken in turn.
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You are not asked to write algorithms in this subject. You are asked to talk about what happens when one is put in charge of something.
Real-world examples you can name
YouTube's recommendation system — deep-learning system described 2016; borderline-content changes from January 2019
A ranking system that predicts what a viewer will watch next, which the company has said drives the majority of watch time. After criticism that it led viewers towards ever more extreme material, the company began demoting 'borderline' content rather than removing it.
Who it affected: Roughly two billion monthly viewers, and creators whose income depends on the ranking.
COMPAS recidivism scoring — ProPublica investigation May 2016
A commercial risk-scoring tool used in some US courts to estimate how likely a defendant was to reoffend. Journalists found that among people who did not go on to reoffend, Black defendants were roughly twice as likely as white defendants to have been labelled high risk. The company disputed the measure of fairness used, and the argument that followed showed that competing definitions of a fair algorithm cannot all be satisfied at once.
Who it affected: Defendants whose bail and sentencing decisions were informed by the score.
How this is tested — you have to define an algorithm exactly and name what makes something count as one. It comes up two ways:
Paper 1 — structured question
- Part a: identify characteristics of an algorithm
- Part b: explain why a described rule does or does not qualify
Paper 2 — source-based question
- Q1: identify what a source says a system does
- Q2: explain how a source uses the word “algorithm”
The trap: “an algorithm is a computer program”: A recipe is an algorithm and is not a program. The definition is about defined steps solving a problem, not about computers.
Identify two characteristics of an algorithm.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Explain one reason why efficiency matters when judging an algorithm.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.