CDEI study guide
CDEI · Work through your exam, one topic at a time.
The Certified Data Ethics Expert (CDEI) is a professional credential for people who make, review, or oversee decisions about how data is collected, used, shared, and automated. Candidates typically come from data science and analytics, privacy and compliance, product management, research, and AI governance roles. The CDEI study guide has 8 sections and about 2,360 words.
01Foundations 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.
325 words, about 2 min02Ethical Frameworks and Theories
Ethical frameworks give candidates a vocabulary for justifying decisions. The core families are consequentialism (judge acts by outcomes - e.g., utilitarianism maximizing aggregate welfare), deontology (duty- and rule-based, honoring rights and treating people as ends, associated with Kant), virtue ethics (character and dispositions - what would a prudent, honest practitioner do?), and justice/fairness theories (distributive fairness, Rawls's veil of ignorance, and care ethics emphasizing relationships and vulnerability).
300 words, about 2 min03Data Privacy and Confidentiality
Privacy concerns who may access and control information about a person ; confidentiality concerns the duty to protect information shared in trust . Candidates must grasp identifiability (direct identifiers, quasi-identifiers, and re-identification via linkage), de-identification and anonymization (and why k-anonymity, l-diversity, and differential privacy are tools with trade-offs, not guarantees), purpose limitation , data minimization , retention limits , and contextual integrity - information flows are appropriate when they match the norms of the context in which data was shared.
282 words, about 1 min04Bias, Fairness, and Equity in Data
Bias enters data systems at every stage: historical/societal bias baked into labels, sampling and selection bias , measurement bias (proxies that misrecord reality), label bias , and deployment bias (misuse of a model in a new context). Candidates must know the fairness metrics and, crucially, that they cannot all hold at once.
302 words, about 2 min05Data Governance and Stewardship
Data governance is the system of decision rights, roles, policies, and controls that determine how data is managed and used. Candidates should understand roles (data owners, data stewards, data custodians, controllers, processors), accountability structures (governance councils, review boards, DPOs), and the data lifecycle - acquisition, storage, use, sharing, retention, and disposal.
290 words, about 1 min06Transparency, Explainability, and Accountability
This domain covers how organizations make data and algorithmic decisions visible, understandable, and answerable . Candidates must distinguish transparency (disclosure of data, methods, and decisions), explainability (why a specific output occurred), and accountability (who answers and how redress is provided).
292 words, about 1 min07Regulatory and Legal Landscape for Data Ethics
Candidates must recognize the major legal frameworks and their core principles without treating any single statute as universal. Know the GDPR pillars - lawful basis, purpose limitation, data minimization, storage limitation, accuracy, integrity/confidentiality, accountability, data-subject rights (access, rectification, erasure, portability, objection), and the special treatment of sensitive data.
285 words, about 1 min08Applied Data Ethics in AI and Analytics
Applied ethics turns principles into practice across the AI lifecycle. Candidates should know ethical impact assessments and algorithmic impact assessments , privacy-by-design and ethics-by-design , human-in-the-loop review, model documentation (model cards, datasheets), red-teaming and auditing , and monitoring for drift and unintended use .
284 words, about 1 min