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A professional-level summary covering key definitions, frameworks, and exam-relevant points.
Data Ethics Principles and Applications
| Principle | Definition | Practical Application |
|---|---|---|
| Transparency | Openness about data practices | Clear privacy policies; data use notices; explainable AI |
| Fairness | Non-discrimination in data and algorithms | Bias testing; diverse training data; fairness audits |
| Accountability | Responsibility for data outcomes | Data ownership; governance frameworks; audit trails |
| Privacy | Respect for individual privacy rights | Privacy by design; consent management; data minimisation |
| Beneficence | Using data for good | Ethical review boards; impact assessments; responsible AI |
| Data minimisation | Collecting only necessary data | Purpose limitation; retention policies; privacy impact assessments |
CDMP Exam Relevance
Data Ethics is a dedicated knowledge area in the CDMP exam (approximately 2% weight). Key exam topics include: the core principles of data ethics, the relationship between data ethics and data governance, the ethical challenges posed by big data and AI, the concept of algorithmic bias and how to address it, and the difference between legal compliance and ethical behaviour in data management. Data ethics questions often appear in the context of data governance and data quality knowledge areas as well.
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