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A strong computing evaluation connects a technical decision to affected people. Explain benefits, risks and feasible safeguards in the particular case instead of presenting a list of disconnected opinions.
What you'll be able to do
Stakeholders can include users, people represented in a dataset, employees, owners, communities and people excluded from a service. A feature that helps one group can burden another. Ask who benefits, who carries the risk and who had a meaningful opportunity to influence the decision.
Moral questions concern individual judgements and responsibilities; social and ethical discussion considers wider relationships and shared values. Cultural assumptions can enter categories, interfaces and training data. An algorithm is not automatically neutral merely because its operations are written mathematically.
Monitoring and combining personal records can support useful services but can also reveal sensitive patterns, enable discrimination or reduce privacy. Automated ranking may amplify a biased measurement or an unrepresentative dataset. Scale can turn a small per-person error into a large overall impact.
Digital access, disability support, language and connectivity affect who can use a service. Environmental evaluation can include manufacturing, energy, replacement and disposal rather than only whether a device uses less paper. Link each consequence to the actual proposed data flow or feature.
Consider data minimisation, access controls, transparent explanations, user choice, accessible design, auditing and a route to challenge important decisions. Explain what each safeguard reduces and which residual risks remain. A technical measure cannot resolve every disagreement about the underlying purpose.
An extended response should reach a justified position under stated assumptions. Weigh the importance and likelihood of effects, not merely count positive and negative points. Law can constrain a decision, but legal permission alone does not settle whether it is fair, culturally appropriate or desirable.
| Question | Example focus |
|---|---|
| Who is affected? | Applicants and decision-makers |
| What mechanism creates the effect? | Ranking based on historical outcomes |
| What could reduce risk? | Representative evaluation and an appeal route |
| What remains uncertain? | Performance across different groups |
Attempt the task before opening its worked response. Record each change of state, explain the rule used, and check the result against the original requirements. Code examples labelled Python 3 use that language’s conventions; pseudocode states its own assumptions.
Common mistakes to avoid
Key takeaways
Test yourself
Answer a few questions, then review anything you missed.
Start with the quiz above to check the key ideas. You can also choose another topic from the practice library.