Impact of AI
AI Data Quality and Bias
Many AI systems depend on large amounts of example data, often called training data. The quality of that data strongly affects how reliable and fair the AI's outputs are.
When the data is poor
If the example data is incomplete, the AI has not seen some situations and may handle them badly. If it is outdated, the AI can give answers that were once true but are no longer correct. If it is biased, it over-represents some people or cases and under-represents others, so its decisions can be unfair.
| Problem with the example data | Effect on the AI | Example |
|---|---|---|
| Incomplete | Missing cases are handled badly or filled in wrongly | A plant-identification app trained without pictures of young seedlings may label a seedling as the wrong plant. |
| Outdated | Old patterns are applied to a changed world | An AI that answers questions about local bus times, trained on last year's timetable, sends you to a stop that is no longer used. |
| Biased | Some groups are favoured or recognised less accurately | A handwriting-recognition system trained mostly on writing from right-handed people may read left-handed users less accurately, so they are marked wrong more often. |
Bias is not always added on purpose. It can come from the examples chosen: if one group appears far more often in the data, the AI picks up that group's patterns as normal and treats other patterns as unusual.
Wrong answers given confidently
AI can produce an answer that looks clear, detailed and certain but is still wrong. It does not automatically check facts and cannot tell when its data does not cover the question. A generative AI tool asked about a town's old cinema might invent an opening date and present it neatly as fact. Treat an AI output as a first draft to check against a reliable source, not as proof.
Practise this
OCR GCSE Computer Science · Impact of AI