CRAGE
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ECCouncil
The CRAGE certification teaches professionals AI governance fundamentals, ethics, bias mitigation, transparency, and accountability, enabling them to design and oversee responsible AI programs that meet regulatory and ethical standards.
120
Minutes
50
Questions
70/100
Passing Score
$250
Exam Cost
Who Should Take This
Mid‑level to senior AI strategists, data scientists, compliance officers, and technology managers who have several years of experience in AI development or deployment should pursue CRAGE. They aim to institutionalize ethical AI practices, ensure transparent decision‑making, and demonstrate accountability to stakeholders and regulators.
What's Covered
1
AI Governance Fundamentals
2
AI Ethics
3
Bias and Fairness
4
Transparency and Explainability
5
Accountability
6
Privacy and Data Rights
7
Regulatory Compliance
8
Risk Management
9
Organizational Implementation
10
Metrics and Continuous Improvement
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
60 learning goals
1
AI Governance Fundamentals
2 topics
Governance frameworks
- Apply AI governance frameworks including NIST AI RMF OECD AI Principles IEEE EAD and organizational AI governance structures.
- Analyze governance framework suitability to evaluate organizational fit regulatory alignment and implementation requirements.
- Design AI governance programs incorporating framework selection organizational structure and oversight mechanism development.
Policy development
- Apply AI policy development including acceptable use guidelines development standards and deployment approval processes.
- Analyze AI policy effectiveness to evaluate coverage compliance adherence and enforcement mechanism sufficiency.
- Design AI policy frameworks incorporating lifecycle coverage stakeholder input and continuous review and update processes.
2
AI Ethics
2 topics
Ethical principles
- Apply AI ethical principles including beneficence non-maleficence autonomy justice and human dignity in AI system design decisions.
- Analyze ethical implications of AI systems to evaluate potential harms stakeholder impacts and value alignment concerns.
- Design ethical assessment frameworks incorporating principle evaluation stakeholder impact analysis and values alignment review.
Ethical decision-making
- Apply ethical decision-making frameworks to resolve conflicts between competing values in AI system design and deployment.
- Analyze ethical dilemmas in AI to evaluate trade-offs between accuracy fairness privacy and utility for resolution guidance.
- Design ethical review processes incorporating ethics board consultation impact assessment and stakeholder engagement mechanisms.
3
Bias and Fairness
2 topics
Bias detection
- Apply bias detection methods including statistical parity equalized odds calibration and intersectional fairness metrics for AI systems.
- Analyze bias patterns to identify sources including training data representation feature selection and model architecture influences.
- Design bias detection programs incorporating automated testing continuous monitoring and demographic impact assessment processes.
Bias mitigation
- Apply bias mitigation techniques including pre-processing in-processing and post-processing methods for fair AI system development.
- Analyze mitigation effectiveness to evaluate fairness improvement accuracy trade-offs and residual bias measurement results.
- Design bias mitigation strategies incorporating technique selection stakeholder validation and continuous fairness monitoring.
4
Transparency and Explainability
2 topics
Model explainability
- Apply explainability techniques including LIME SHAP feature importance attention visualization and counterfactual explanations.
- Analyze model explanations to evaluate faithfulness completeness and appropriateness for different stakeholder audiences.
- Design explainability programs incorporating technique selection audience adaptation and documentation standards for AI systems.
Documentation standards
- Apply AI documentation standards including model cards datasheets and system-level documentation for transparency.
- Analyze documentation completeness to evaluate coverage accuracy and accessibility for technical and non-technical audiences.
- Design AI documentation programs incorporating model cards data sheets and system documentation templates and review processes.
5
Accountability
2 topics
Accountability frameworks
- Apply AI accountability frameworks including responsibility assignment audit trails and decision provenance for AI systems.
- Analyze accountability gaps to identify insufficient oversight missing audit mechanisms and unclear responsibility assignment.
- Design accountability programs incorporating clear ownership audit capability and escalation procedures for AI decisions.
Human oversight
- Apply human oversight mechanisms including human-in-the-loop human-on-the-loop and human-over-the-loop for AI system supervision.
- Analyze oversight effectiveness to evaluate intervention capability decision support quality and supervision adequacy.
- Design human oversight frameworks incorporating appropriate intervention levels monitoring dashboards and override procedures.
6
Privacy and Data Rights
2 topics
AI privacy
- Apply privacy principles in AI including data minimization purpose limitation consent management and differential privacy techniques.
- Analyze AI privacy risks to evaluate data exposure model memorization and inference attack vulnerability in AI systems.
- Design AI privacy programs incorporating privacy-by-design differential privacy and data rights management for AI systems.
Data rights
- Apply data subject rights in AI including right to explanation right to contest automated decisions and data deletion requests.
- Analyze data rights compliance to evaluate automated decision transparency contest mechanisms and deletion capability.
- Design data rights frameworks for AI incorporating explanation generation contest procedures and deletion verification processes.
7
Regulatory Compliance
2 topics
EU AI Act
- Apply EU AI Act requirements including risk classification conformity assessment documentation and prohibited AI practice identification.
- Analyze EU AI Act applicability to evaluate organizational AI systems for risk tier compliance obligations and gap remediation.
- Design EU AI Act compliance programs incorporating risk classification assessment procedures and continuous monitoring frameworks.
Global regulations
- Apply global AI regulations including NIST AI RMF OECD guidelines and sector-specific AI requirements across jurisdictions.
- Analyze multi-jurisdictional AI compliance to identify regulatory conflicts harmonization opportunities and risk-based approaches.
- Design global AI compliance strategies incorporating regulatory monitoring cross-border coordination and adaptive compliance programs.
8
Risk Management
2 topics
AI risk assessment
- Apply AI risk assessment using NIST AI RMF including map measure manage and govern functions for organizational AI risk.
- Analyze AI risks across technical ethical social and organizational dimensions for comprehensive risk evaluation.
- Design AI risk management frameworks incorporating continuous assessment treatment strategies and risk communication processes.
Impact assessment
- Apply AI impact assessment including human rights impact social impact and environmental impact evaluation for AI systems.
- Analyze impact assessment results to prioritize mitigation actions stakeholder engagement and monitoring requirements.
- Design impact assessment programs incorporating stakeholder consultation measurement methodologies and ongoing monitoring.
9
Organizational Implementation
2 topics
AI governance structure
- Apply AI governance organizational design including ethics boards review committees and cross-functional governance teams.
- Analyze governance structure effectiveness to evaluate decision-making quality oversight coverage and organizational integration.
- Design AI governance organizations incorporating board composition meeting cadence decision authority and escalation procedures.
Training and awareness
- Apply responsible AI training programs including developer education executive awareness and organization-wide AI literacy.
- Analyze training effectiveness to evaluate knowledge retention behavioral change and responsible AI practice adoption.
- Design responsible AI education incorporating role-based curricula practical exercises and continuous learning programs.
10
Metrics and Continuous Improvement
2 topics
Governance metrics
- Apply AI governance metrics including compliance rates bias measurements transparency scores and stakeholder satisfaction indicators.
- Analyze governance metrics to identify improvement trends evaluate program effectiveness and inform resource allocation.
- Design AI governance measurement programs incorporating balanced metrics dashboard reporting and executive communication.
Maturity advancement
- Apply responsible AI maturity assessment to evaluate organizational capabilities and develop structured improvement roadmaps.
- Analyze maturity levels to prioritize governance investments identify quick wins and plan long-term capability development.
- Design maturity advancement strategies incorporating phased implementation success criteria and continuous evaluation cycles.
Scope
Included Topics
- EC-Council CRAGE covering responsible AI governance including ethics frameworks bias mitigation transparency accountability and regulatory compliance.
- AI governance frameworks including organizational structure policy development and oversight mechanisms for AI systems.
- AI ethics including fairness bias detection mitigation strategies and inclusive design principles for AI development.
- AI transparency and explainability including model interpretability documentation and stakeholder communication practices.
- AI regulatory compliance including EU AI Act NIST AI RMF and industry-specific AI governance requirements.
Not Covered
- AI fundamentals covered by AIE.
- AI security testing covered by COASP.
- AI project management covered by CAIPM.
- Enterprise security governance covered by CCISO.
Official Exam Page
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