Certified Ethical Emerging Technologist Exam Prep

Covers the ethical concepts, theories, and decision-making methods used in AI-related business contexts. It also addresses how to identify and prioritize ethical risks arising from AI design, deployment, and use, especially when business incentives create tension with ethical responsibilities.

• Task 1.1: Explain core ethical concepts used in AI-related business decisions
• Define core ethical concepts
• Legal obligations versus ethical responsibilities
• Common AI ethics principles
• Ethical concerns across the AI lifecycle
• Task 1.2: Apply major ethical theories to AI business scenarios
• Compare major ethical theories

Key references: CET-110 official exam guide · ExamPal shared topic tree

Covers how bias enters AI systems, how fairness is evaluated, and how bias mitigation is applied across the AI lifecycle. It also addresses the societal effects of AI-driven content and the communication of fairness issues to stakeholders.

• Task 2.1: Identify sources of bias in AI systems and data pipelines
• Bias in data pipelines
• Types of bias
• Business processes and human judgments
• Bias risks in high-impact use cases
• Task 2.2: Evaluate fairness considerations for AI-enabled business decisions
• Common fairness goals

Key references: CET-110 official exam guide · ExamPal shared topic tree

Covers ethical data collection and use, privacy risk reduction, retention and lifecycle decisions, and user rights in AI-enabled services. It also addresses coordination of privacy decisions across business functions and privacy-by-design practices.

• Task 3.1: Apply ethical principles for responsible data collection and use
• Core data collection principles
• Necessity of proposed collection
• Secondary uses beyond expectations
• Sensitive, personal, and inferred data
• Task 3.2: Recommend methods to reduce privacy risk in AI initiatives
• Privacy risk reduction techniques

Key references: CET-110 official exam guide · ExamPal shared topic tree

Covers transparency and explainability expectations, tensions between openness and organizational interests, governance structures for oversight, and post-deployment monitoring and response. The domain emphasizes accountability through roles, decision rights, documentation, and corrective action.

• Task 4.1: Explain transparency and explainability expectations for AI systems
• Transparency-related concepts
• When explanation is necessary
• Limits on disclosure
• Practical transparency measures
• Task 4.2: Evaluate tensions between transparency and organizational interests
• Openness versus intellectual property

Key references: CET-110 official exam guide · ExamPal shared topic tree

Covers alignment with external standards, the distinction between regulation and voluntary commitments, and how to reconcile business goals with ethical and regulatory constraints. It also addresses integrating ethics into strategy, workflows, metrics, and organizational culture.

• Task 5.1: Align AI ethics programs with recognized standards and guidance
• Purpose of external frameworks
• Alignment supports consistency and credibility
• Map internal policies to standards
• Benchmark maturity and identify gaps
• Task 5.2: Distinguish regulatory requirements from voluntary ethical commitments
• Compare legal and voluntary commitments

Key references: CET-110 official exam guide · ExamPal shared topic tree