Automation removes routine tasks from jobs and creates new work elsewhere. The problem is not the total — it is the gap between those two places, in skills and in geography.
Four responses to automation, and what each one reaches.
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| Response | What it does | When it fails |
|---|---|---|
| Retrain | Moves people to the new work | When the new work is elsewhere, or there is no time or money to train |
| Redistribute | Taxes the gains to support the displaced | It reaches everybody and restores nobody's role |
| Reduce hours | Spreads the remaining work further | It took decades last time, and needs pay to hold |
| Redesign | Changes what gets automated | Nobody currently decides this deliberately |
Why redesign is the interesting one: The other three respond to the change. Redesign changes the change — automating the parts of a job nobody wants rather than the parts that are cheapest to replace.
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Real-world examples you can name
Platform work and employment status — UK Supreme Court ruling on Uber drivers February 2021; EU platform work directive adopted 2024
Work is allocated by an app rather than a manager. The UK Supreme Court held that Uber drivers are workers entitled to minimum wage and holiday pay, and the EU has since legislated a presumption of employment where a platform controls the work.
Who it affected: Couriers and drivers, and companies whose model assumed self-employment.
Amazon's scrapped CV-screening tool — reported October 2018
An experimental hiring tool was trained on a decade of CVs submitted to the company, most of them from men. It learned to downgrade CVs containing the word 'women's' and graduates of two women's colleges. The project was abandoned.
Who it affected: Women applying for technical roles, and every organization that assumed historical data was neutral.
Boston Dynamics' Spot — commercial sale from 2020
A four-legged robot that walks over ground wheels cannot manage, used for inspecting construction sites, refineries and nuclear facilities. Police trials prompted objections strong enough that some forces returned it.
Who it affected: Inspection workers kept out of hazardous places, and communities uneasy about policing use.
Points worth using
- Entry-level tasks go first, and they were how people learned the job — so the path in closes before the job does.
- New work is real and smaller, and it needs different skills in different places.
- Bargaining power falls before employment does, because a worker whose tasks could be automated negotiates from a weaker position whether or not they are.
- Retraining works where work exists nearby, which is exactly where it is least needed.
How this is tested — this is the most frequently examined HL challenge, and almost every answer reaches for retraining without defending it. It comes up two ways:
Paper 1 Section B — extended response
- Extended response on automation and employment
- Concepts — power and change — named explicitly
Paper 3 — the intervention paper
- Recommend a response for a described region or industry
- Justify it against the alternatives you rejected
The trap: retraining by default: Retraining needs nearby work, time and money. Say whether those hold in your case, or recommend something else and say why.
Explain one reason why retraining programmes often fail to help displaced workers.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
Recommend one response to automation in a region where one large employer has automated most routine work.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.