AI Definitions, Risks and Responsible Principles
AIGP candidates must know accepted AI definitions, why AI requires governance, major AI risk types, AI-specific characteristics, and responsible AI principles.
How to study for IAPP AIGP
Treat each question as a governance decision: identify the AI role and life-cycle stage, classify the legal or risk issue, then choose the control, evidence, and accountability path.
Core concepts
Concept 1
AI governance starts with a shared vocabulary for AI systems, model behavior, data dependency, autonomy, opacity, speed, scale, and probabilistic outputs.
Exam cue: Classify whether the issue is a definition, a risk characteristic, or a responsible AI principle.
Concept 2
AI harms can affect individuals, groups, organizations, and society through bias, misalignment, misuse, privacy loss, security failures, and unreliable decisions.
Exam cue: Use probabilistic output and opacity when explaining why ordinary software controls may not be enough.
Concept 3
Responsible AI principles commonly include fairness, safety, reliability, privacy, security, transparency, explainability, accountability, and human-centered design.
Exam cue: Connect harms to affected stakeholders, not just technical model performance.
Risk pitfalls and guardrails
Treating AI governance as only a technical model-quality problem.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Assuming responsible AI principles are met just because a system is accurate overall.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Ignoring group or societal harms when the immediate user appears satisfied.
Guardrail: Avoid treating vendor tools as risk-free, relying on aggregate accuracy alone, or stopping governance after deployment approval.
Memory anchors
AI Governance
AI governance is the system of roles, policies, controls, and oversight used to manage AI responsibly.
Probabilistic Output
Probabilistic output means AI results may vary and require risk-aware interpretation.
Opacity
Opacity is limited visibility into why an AI system produced a result.
Misalignment Risk
Misalignment risk occurs when an AI system's behavior does not match intended objectives or values.
Bias Risk
Bias risk is the chance that data, design, or deployment choices create unfair outcomes.
Responsible AI
Responsible AI applies principles such as fairness, safety, privacy, transparency, and accountability.
Human-Centricity
Human-centricity keeps affected people, rights, and meaningful oversight in the governance frame.
Accountability
Accountability assigns ownership for AI decisions, controls, monitoring, and remediation.
Checkpoint rule
Do the check-up only after you can summarize each concept in one sentence and identify one dangerous pitfall from memory.
Knowledge Check (after reading)
Short check-up to confirm understanding of this module.
Check-up Questions
What best describes artificial intelligence as used in AI governance?
What is machine learning?
Answer all questions to submit.
Next step personalized recommendations
Continue learning
Move forward only after this module is stable.
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