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OCR H446 1.5.2 Analysing and monitoring personal information

Part 5 of 9 · H446 1.5.2 · Moral and ethical issues

In the monitoring scenarios of OCR H446 1.5.2, the harm usually begins at analysis rather than at collection. Students follow personal data from collection through combination and inference to a decision and its consequence, using insurance pricing, a learning platform's effort label and shopping-centre tracking.

Students will:

  • trace personal data from what was collected to the decision it eventually produces
  • explain how accurate raw data can still support an unfair inference
  • judge whether a person would expect, notice or be able to challenge a use of their data
  • propose safeguards covering accuracy, purpose and a practical route of appeal
  • discuss the implications of combining several tracking sources in one public space

Inside: 5 explanation cells, 1 multiple-choice question, 2 fill-in-the-blanks cells and 2 written answers. 26 marks, about 30 to 50 minutes.

Series: H446 1.5.2 · Moral and ethical issues, part 5 of 9.

Shared by Coding PathwayVerified teacher

  • 10 cells
  • About 45 minutes
  • CC BY-SA 4.0
  • Shared 31 Aug 2026
  • Updated 3 Sept 2026

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The whole resource, exactly as a class sees it. Answers and marking are held back.

Analysing and monitoring personal information

This worksheet examines what organisations can learn and decide from personal information. When a question asks about the use of personal information, explain what data is collected and what the organisation concludes. Then explain what decision follows and how it affects a person.

From data to a real effect

Personal-information inference chainanalysis can create new information about a personCOLLECTclicks + locationINFERinterest / riskDECIDEprice / accessAFFECTopportunityfour checkpointsaware and legitimate?relevant and secure?inference accurate and fair?explanation, correction or appeal?ask who controls the data, who is responsible and who is affected

Organisations can combine browsing, location, purchase, communication or sensor data to make a new claim about someone’s interests, health, risk or identity. This kind of conclusion is called an inference. It can help personalise services or detect problems. It can also be wrong, unexpected, intrusive, unfair or used for a different purpose.

When an examination question asks about the use of personal data, a strong answer follows the whole route: data collected → inference made → decision taken → effect on a person. Then ask whether the person knew, whether the data and inference were accurate, who was responsible, and whether the person could correct or challenge the result.

Worked example: insurance inference

A fitness app shares a user’s late-night location pattern. An insurer assumes this shows risky behaviour and increases the user’s price. The location records might be accurate, but the assumption could still be wrong: the person may work night shifts. The user might not expect fitness data to be used for insurance and may have no practical way to challenge the decision.

Useful safeguards include a clear purpose, relevant data, checks on the inference, an explanation, a correction route and human review.

Multiple choice1 mark

Why could accurate location data still lead to an unfair insurance decision?

  • AThe insurer may make an unreliable assumption about why the person was at those locations
  • BThe insurer may store the location data securely before using it to make the decision
  • CThe person may have agreed that the fitness app could record location during exercise
  • DThe location records may use a standard data format that the insurer can process
Fill in the blanks4 marks
Personal data is gap 1 and combined to create an gap 2. That inference may be used in a gap 3 with a real consequence. People may need an explanation, a correction or an gap 4.
  • collected
  • inference
  • decision
  • appeal
  • piracy
Written answer8 marks

A learning platform labels students as showing ‘low effort’ when they log in late at night. Teachers can see the label. Explain two possible harms. For each harm, suggest one safeguard.

Consider why a student might log in late, how a teacher could react to the label, who should see it and whether the student can correct it.

Students type their answer here.

Apply the ideas independently

The next task uses a new collection of personal information. Follow it from the recorded data to the inference, decision and effect.

Written answer9 marks

A shopping centre combines Wi-Fi locations, face matches and purchase records to predict which visitors should receive extra security attention. Discuss the issues raised by this system.

Use details from the scenario. Consider safety, incorrect matches or assumptions, privacy, awareness, security of the data, unequal effects and who is responsible. Finish with a supported conclusion.

Students type their answer here.

Fill in the blanks4 marks
Data analysis can create a new checkpoint gap 1 about a person. Check whether it is based on relevant and checkpoint gap 2 information, whether the resulting decision is fair, and whether the person can ask for an explanation, correction or checkpoint gap 3. The organisation must remain checkpoint gap 4 for the outcome.

Review your responses

Check that you separated recorded facts from assumptions and explained the real effect of each decision. Make responsibility and challenge routes clear.