Foundations of Data Ethics
Data ethics is the study of how values, rights, and obligations apply to the collection, analysis, sharing, and use of data. A candidate should be able to distinguish ethics from law and from compliance: law sets a floor of enforceable obligations, while ethics asks what is right even where no rule compels a particular act. Key concepts include informed consent, purpose limitation, data minimization, proportionality, harm, dignity, autonomy, and group versus individual harm. Understand the difference between a data subject, a data controller, and a data processor, and why responsibility scales with control over purpose.
Traps in exam questions: (1) conflating legal with ethical - a practice can be lawful yet ethically questionable, and vice versa; (2) assuming anonymization eliminates ethical duties (re-identification risk and group harms persist); (3) treating consent as a cure-all when power imbalances or secondary uses undermine genuine choice; (4) ignoring downstream or secondary harms to people who never interacted with the system.
Rule of thumb: when a scenario has no clear rule, ask three questions - Who could be harmed? Do they know and can they object? Would I be comfortable if the affected person saw how their data was used? This "sunlight test" surfaces ethical issues that compliance checklists miss.
Worked example: A retailer uses loyalty-card data to infer pregnancy and target ads. It may be legal under some regimes, but the ethical analysis flags inferred sensitive attributes, lack of meaningful notice, and potential distress - pushing toward opt-in and suppression of sensitive inferences.