The guide groups the data dilemmas into three, and every exam question in this area sits in one of them. Knowing which one you are in tells you what the counter-argument has to be.
The three
Is it any good?
Bias, reliability and integrity. Was the data collected from everyone? Can it be trusted? Has it been changed since?
Whose is it?
Control, ownership and access. Who decides what happens to data about you \u2014 you, the company holding it, or the state?
Who can see you?
Privacy, anonymity and surveillance, and personally identifiable information \u2014 the details that point back to one named person.
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Removing names does not make data anonymous. Enough details together point at one person even when no field says who they are.
How “anonymous” data gets re-identified
- A few details are enough. Postcode, date of birth and sex together identify most individuals.
- Joining datasets. Two harmless releases overlap and the overlap names people.
- Patterns are personal. Where a phone sleeps every night and where it spends every weekday is a signature.
- Rare things stand out. An unusual job, an unusual journey or an unusual condition identifies on its own.
The line worth learning: Data does not have to contain your name to be about you. That single sentence answers a surprising number of part b questions.
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Three cases, one for each family \u2014 and each one is still being argued about, which is what makes them useful in a part c answer.
Real-world examples you can name
Facebook and Cambridge Analytica — disclosed March 2018; US FTC penalty July 2019
A personality-quiz app collected data not only from the people who installed it but from their friends, reaching tens of millions of profiles, which were then used to target political advertising. Facebook was fined US$5 billion by the FTC over its privacy practices.
Who it affected: Facebook users who never installed the app, and voters targeted by the campaigns.
Google Spain v AEPD — the right to be forgotten — Court of Justice of the EU, May 2014
A Spanish man asked for search results about a long-settled debt to stop appearing under his name. The court held that a search engine is a data controller and can be required to de-list results that are inadequate, irrelevant or excessive — without the original page being deleted.
Who it affected: Anyone in the EU asking a search engine to de-list results about them.
San Francisco's facial recognition ban — May 2019
The first major US city to bar its own agencies, including the police, from using facial recognition — a rule about government use, not about the technology existing. Several other cities followed, and a few later relaxed theirs.
Who it affected: Residents, and police forces that had to find other methods.
Why a dilemma is not just a problem: In each of these, the thing that causes the harm is the same thing that makes the system work. Removing it removes the benefit too, which is why they are argued about rather than simply fixed.
How this is tested — this is the part of the topic that carries the 8- and 12-mark questions, so it is where most of the marks in 3.1 actually are. It comes up two ways:
Paper 1 — structured question
- Part c: evaluate the opportunities and dilemmas of a data system
- Part b: explain a concern about how data is collected or shared
Paper 2 — source-based question
- Q3: contrast two sources on who benefits from a dataset
- Q4: synthesise sources on the dilemmas of a described system
The trap: writing a warning instead of an argument: A dilemma has two sides that cannot both be fully satisfied. If your answer only lists harms, you have written about a problem, and the markband will place it accordingly.
Discuss the impacts and implications of a city collecting detailed movement data from public transport cards.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.