Data Governance, Privacy & Ethics
Use data lawfully, proportionately and transparently while protecting privacy, security and affected populations. The topic is split into focused lessons so definitions, implementation decisions, diagnostics and common failure modes can be learned separately.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Data minimisationCollect and retain only information necessary for the stated purpose. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
02Privacy and de-identificationPrivacy controls reduce the risk that individuals can be identified or their information misused. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
03Consent, purpose and accessData use should align with consent, lawful authority, organisational policy and purpose limitations. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
04Fairness and representativenessEvaluate whether data coverage and system behaviour differ systematically across relevant groups. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.
05Documentation and accountabilityDocument datasets, assumptions, intended use, limitations, approvals and model decisions. The important practical question is not only how the technique is defined, but what assumptions it introduces, which data are allowed to influence it, and how its effect should be validated on unseen evidence.