Knowledge check
Evaluate your understanding of this unit by completing the knowledge check
These questions summarise core themes from ethical frameworks, privacy law, responsible data practice and governance.

Action item: Knowledge check
Answer each question below. Correct answers and short feedback are included for self-review.
- A. Accountability
- B. Data minimisation
- C. Storage limitation
- D. Integrity and confidentiality
Feedback: Storage limitation is about retention duration; minimisation focuses on collecting only what you need.
- A. Replace the need for security controls
- B. Identify and mitigate privacy risks before processing begins
- C. Prove the model is unbiased
- D. Eliminate the need for lawful bases
Feedback: A DPIA is a structured privacy risk assessment; it complements fairness work but does not replace it.
- A. Training models without centralising raw training data on one server
- B. Removing the need for encryption
- C. Guaranteeing that model updates cannot leak any information
- D. Avoiding documentation of data lineage
Feedback: Federated learning reduces central data aggregation; it still requires secure aggregation and threat modelling.
- A. Maximising average happiness regardless of rules
- B. Letting the highest-paid stakeholder decide
- C. Collecting as much data as possible
- D. Following clear ethical rules and duties even when inconvenient
Feedback: Deontology is rule- and duty-driven rather than purely outcome-maximising.
- A. The model runs too slowly
- B. A non-representative time window dominates the dataset
- C. Timestamps were encrypted incorrectly
- D. The dataset has too many categorical variables
Feedback: Crisis periods or one-off events can distort “normal” behaviour if not handled carefully.
- A. Skip transparency if the model is accurate
- B. Avoid documenting the purpose of processing
- C. Perform and record a balancing test against individuals' rights
- D. Assume legitimate interests applies to any AI use case
Feedback: Legitimate interests is not automatic; necessity and balancing are core.
- A. A proactive review of benefits, harms and mitigations before deployment
- B. A marketing review of model branding
- C. A substitute for security patching
- D. A one-line disclaimer in a privacy policy
Feedback: AIIAs help teams anticipate fairness, privacy and societal risks early.
- A. A ban on all neural networks
- B. A single accuracy metric for every sector
- C. Exempting healthcare AI from oversight
- D. A risk-based framework with obligations scaled to AI system risk
Feedback: Higher-risk systems face stronger governance, documentation and monitoring expectations.