“It changed” is not an answer. Change in a system has a shape — steady, growing or tipping — and the shape decides whether any intervention can work.
Steady, growing and tipping drawn on one pair of axes, with what each one means for a fix.
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Steady — change the rules
Something pushes back. A one-off effort is absorbed, so the only thing that lasts is a change to what pushes.
Growing — act early
Each round is bigger than the last. Nothing looks alarming at any single step, so it is almost always noticed late.
Tipping — keep a margin
Pressure builds invisibly, then the system flips and stays flipped. Removing the pressure does not undo it.
Why tipping is the dangerous one: Everything looks fine right up to the moment it does not, and the way back is not the way you came. This is the shape behind most arguments for acting before there is proof.
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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.
Agbogbloshie e-waste site, Accra — scrapyard cleared July 2021
A scrapyard where imported electronics were stripped and cables burned to recover copper, releasing lead and dioxins. It became the standard example of e-waste exported from rich countries to poor ones. Authorities demolished much of the site in 2021; the trade moved rather than stopped.
Who it affected: Workers and nearby residents exposed to heavy metals and smoke.
WannaCry and the NHS — May 2017
Ransomware spread through a Windows flaw for which a patch already existed, encrypting files and demanding payment. In England it disrupted around a third of hospital trusts; roughly 19,000 appointments and operations were cancelled.
Who it affected: Patients whose care was postponed, and organizations running unpatched systems.
| Example | Shape | What that implies |
|---|---|---|
| A recommender learning from itself | Growing | Catch it early or not at all |
| E-waste piling up in one place | Growing | Small steps, large total |
| Malware spreading across a network | Tipping | Contained, then everywhere at once |
How this is tested — part c often asks whether something should be acted on now, and the shape of the change is the reason. It comes up two ways:
Paper 1 — structured question
- Part c: evaluate acting before there is full evidence
- Part b: explain why a problem grew unnoticed
Paper 2 — source-based question
- Q4: synthesise sources disagreeing about urgency
- HL Paper 3: recommend, with timing justified
The trap: treating all change as steady: Most answers assume you can act later. For a growing or tipping change that assumption is the mistake the question is testing.
Explain one reason why a growing problem is often noticed late.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
To what extent should governments act on a digital risk before there is clear evidence of harm?
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.