ISO 24028 — Overview of Trustworthiness in AI — Certification Brochure

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London Academy of Professional Training
Certificate
ISO 24028 — Overview of Trustworthiness in AI
ISO Certification Programme
REF: IIT-AII-24028  •  ISO IT & Related Technologies  •  Artificial Intelligence
6Subjects 30Chapters 150Classes 430Total Marks 60%Pass Mark LifetimeValidity
At a Glance
ReferenceIIT-AII-24028
AwardCertificate
Assessment430 marks — 258 to pass (60%)
PrerequisitesNone
ValidityLifetime
Awarding BodyLAPT — London Academy of Professional Training
Curriculum
1 Case Studies in Trustworthy AI Deployment 5 chapters  45 marks
• 1 Understanding Trustworthiness in AI: Principles and Frameworks
• 2 Assessing AI Systems: Metrics for Trustworthiness
• 3 Case Studies: Successful Deployment of Trustworthy AI
• 4 Challenges in Maintaining AI Trustworthiness in Deployment
• 5 Future Trends and Innovations in Trustworthy AI
2 Ethical and Legal Aspects of AI 5 chapters · 30 classes  65 marks
• 1 Foundations of Ethical Considerations in AI — 6 classes
› 1.1 Define Ethical Considerations in AI
› 1.2 Explore the Legal Frameworks Governing AI
› 1.3 Identify Key Ethical Theories Relevant to AI
› 1.4 Analyze Case Studies of Ethical AI Dilemmas
› 1.5 Evaluate the Impact of Bias in AI Systems
› 1.6 Develop a Personal Code of Ethics for AI Use
• 2 Legal Frameworks Governing AI — 6 classes
› 2.1 Explore the Key Legal Principles Governing AI
› 2.2 Examine EU Regulations Impacting AI Development
› 2.3 Analyze the Role of Data Protection Laws in AI
› 2.4 Investigate Compliance Challenges in AI Deployment
› 2.5 Discuss Ethical Implications of AI Regulations
› 2.6 Apply Legal Frameworks to Real-World AI Scenarios
• 3 Bias and Fairness in AI Systems — 6 classes
› 3.1 Identify Sources of Bias in AI Systems
› 3.2 Analyze the Impact of Bias on AI Outcomes
› 3.3 Evaluate Fairness Metrics in AI Applications
› 3.4 Explore Techniques for Mitigating Bias
› 3.5 Discuss Legal Implications of Bias in AI
› 3.6 Develop Strategies for Ensuring Fairness in AI Projects
• 4 Accountability and Transparency in AI Deployment — 6 classes
› 4.1 Define Accountability in AI Systems
› 4.2 Explore the Importance of Transparency in AI Deployment
› 4.3 Identify Key Ethical Principles Related to Accountability
› 4.4 Evaluate Case Studies on AI Transparency Challenges
› 4.5 Develop Strategies for Ensuring Accountability in AI Practices
› 4.6 Create an Action Plan for Enhancing Transparency in AI Projects
• 5 Future Trends in AI Ethics and Legislation — 6 classes
› 5.1 Analyze Emerging Ethical Frameworks in AI Governance
› 5.2 Examine Current Legislative Trends Impacting AI Development
› 5.3 Evaluate Case Studies on AI Trustworthiness and Accountability
› 5.4 Discuss the Role of Stakeholders in Shaping AI Legislation
› 5.5 Identify Challenges in Implementing AI Ethical Standards
› 5.6 Propose Strategies for Future Compliance in AI Ethics
3 Foundations of Trustworthiness in AI 5 chapters · 30 classes  85 marks
• 1 Understanding Trustworthiness in AI Systems — 6 classes
› 1.1 Define Trustworthiness in AI Systems
› 1.2 Identify Key Dimensions of Trustworthiness
› 1.3 Explore Ethical Considerations in AI Development
› 1.4 Analyze Real-World Examples of Trustworthy and Untrustworthy AI
› 1.5 Evaluate Trustworthiness Metrics and Standards
› 1.6 Apply Trustworthiness Principles to AI Project Planning
• 2 Principles of Trustworthy AI: Ethical and Regulatory Frameworks — 6 classes
› 2.1 Define Key Ethical Principles Governing Trustworthy AI
› 2.2 Analyze Regulatory Frameworks for AI Governance
› 2.3 Examine the Role of Fairness in AI Systems
› 2.4 Assess Transparency Requirements for AI Algorithms
› 2.5 Evaluate Accountability Mechanisms in AI Deployment
› 2.6 Apply Ethical Guidelines to a Real-World AI Scenario
• 3 Transparency and Explainability in AI — 6 classes
› 3.1 Define Transparency and Explainability in AI Systems
› 3.2 Explore the Importance of Transparency in AI Decision-Making
› 3.3 Analyze Case Studies of Explainable AI in Real-World Applications
› 3.4 Identify Tools and Techniques for Enhancing Explainability in AI
› 3.5 Develop Strategies for Communicating AI Decisions Transparently
› 3.6 Evaluate the Impact of Trustworthiness on AI Adoption and Use
• 4 Accountability and Governance in AI Development — 6 classes
› 4.1 Define Accountability in AI Development
› 4.2 Explore Governance Frameworks for AI
› 4.3 Identify Stakeholders in AI Accountability
› 4.4 Analyze Case Studies of AI Governance
› 4.5 Develop a Governance Plan for AI Projects
› 4.6 Evaluate the Effectiveness of AI Accountability Measures
• 5 Assessing Trustworthiness: Metrics and Evaluation Methods — 6 classes
› 5.1 Identify Key Metrics for Evaluating AI Trustworthiness
› 5.2 Analyze Different Evaluation Methods for AI Systems
› 5.3 Compare Quantitative and Qualitative Approaches to Trustworthiness Assessment
› 5.4 Implement Trustworthiness Metrics in AI Projects
› 5.5 Evaluate Real-World Case Studies of AI Trustworthiness Assessment
› 5.6 Develop a Framework for Continuous Trustworthiness Monitoring in AI
4 Governance Frameworks for AI 5 chapters · 30 classes  85 marks
• 1 Foundations of AI Governance Frameworks — 6 classes
› 1.1 Define Key Terminology in AI Governance
› 1.2 Identify Stakeholders in AI Governance Frameworks
› 1.3 Explore Principles of Ethical AI Implementation
› 1.4 Analyze Case Studies of AI Governance Models
› 1.5 Evaluate the Role of Regulatory Bodies in AI Governance
› 1.6 Develop a Framework for Assessing Trustworthiness in AI
• 2 Key Components of AI Trustworthiness — 6 classes
› 2.1 Define Trustworthiness in AI: Exploring the Concept
› 2.2 Identify Key Components of AI Trustworthiness Frameworks
› 2.3 Analyze Ethical Implications in AI Governance
› 2.4 Assess the Role of Transparency in AI Systems
› 2.5 Evaluate Accountability Mechanisms for AI Models
› 2.6 Implement Best Practices for Building Trustworthy AI Solutions
• 3 Regulatory Landscape for AI Governance — 6 classes
› 3.1 Identify Key Regulations Impacting AI Governance
› 3.2 Analyze the Role of Data Protection Laws in AI Compliance
› 3.3 Examine International Standards for AI Governance
› 3.4 Assess the Impact of Ethical Guidelines on AI Regulations
› 3.5 Explore Sector-Specific Regulations Affecting AI Implementation
› 3.6 Develop Strategies for Navigating the AI Regulatory Landscape
• 4 Risk Management in AI Systems — 6 classes
› 4.1 Identify Key Risks in AI Systems
› 4.2 Assess Impact and Likelihood of AI Risks
› 4.3 Explore Risk Management Frameworks for AI
› 4.4 Develop Mitigation Strategies for AI Risks
› 4.5 Implement Continuous Monitoring for AI Risk Management
› 4.6 Evaluate and Adapt AI Risk Management Practices
• 5 Implementing Effective AI Governance Practices — 6 classes
› 5.1 Define Key Principles of AI Governance
› 5.2 Identify Stakeholders in AI Decision-Making
› 5.3 Assess Existing AI Governance Frameworks
› 5.4 Develop AI Governance Policies and Procedures
› 5.5 Implement Monitoring and Evaluation Mechanisms for AI
› 5.6 Foster a Culture of Accountability in AI Practices
5 Leadership and Management in AI Initiatives 5 chapters · 30 classes  65 marks
• 1 Understanding Leadership Roles in AI Initiatives — 6 classes
› 1.1 Define Leadership Roles in AI Initiatives
› 1.2 Explore Key Competencies for AI Leaders
› 1.3 Analyze the Impact of Leadership on AI Success
› 1.4 Identify Challenges Faced by AI Leaders
› 1.5 Develop Strategies for Effective Leadership in AI
› 1.6 Evaluate Case Studies on Leadership in AI Initiatives
• 2 Building Trust in AI: Ethical Considerations and Frameworks — 6 classes
› 2.1 Define Key Ethical Principles in AI
› 2.2 Identify Stakeholders in AI Trustworthiness
› 2.3 Analyze Case Studies of Ethical AI Implementation
› 2.4 Develop an Ethical Framework for AI Projects
› 2.5 Evaluate Risks and Benefits of AI Systems
› 2.6 Create a Trust-Building Strategy for AI Leadership
• 3 Strategic Planning for AI Implementation — 6 classes
› 3.1 Identify Key Components of Strategic AI Planning
› 3.2 Assess Organizational Readiness for AI Integration
› 3.3 Develop Measurable Objectives for AI Initiatives
› 3.4 Analyze Stakeholder Roles in AI Strategy Development
› 3.5 Create a Risk Management Framework for AI Projects
› 3.6 Formulate an Implementation Roadmap for AI Solutions
• 4 Navigating Risks and Challenges in AI Leadership — 6 classes
› 4.1 Identify Key Risks in AI Leadership
› 4.2 Evaluate Challenges in Implementing AI Initiatives
› 4.3 Assess the Impact of Ethical Considerations in AI
› 4.4 Develop Strategies for Risk Mitigation in AI Projects
› 4.5 Create a Framework for Trustworthiness in AI Leadership
› 4.6 Implement Action Plans to Address AI Leadership Challenges
• 5 Measuring Success and Accountability in AI Initiatives — 6 classes
› 5.1 Define Success Metrics for AI Initiatives
› 5.2 Identify Key Performance Indicators (KPIs) in AI Leadership
› 5.3 Establish Accountability Structures for AI Projects
› 5.4 Analyze Case Studies of Successful AI Implementations
› 5.5 Develop a Framework for Evaluating AI Outcomes
› 5.6 Create a Reporting Strategy for AI Initiative Progress
6 Risk Management in AI Projects 5 chapters · 30 classes  85 marks
• 1 Understanding AI Risk Management Frameworks — 6 classes
› 1.1 Identify Key Components of AI Risk Management Frameworks
› 1.2 Analyze Historical AI Failures to Understand Risk Factors
› 1.3 Evaluate Existing AI Risk Management Frameworks for Trustworthiness
› 1.4 Develop Risk Assessment Criteria for AI Projects
› 1.5 Implement Strategies for Mitigating AI Risks in Practice
› 1.6 Create an Action Plan for Continuous Risk Monitoring in AI
• 2 Identifying and Assessing Risks in AI Projects — 6 classes
› 2.1 Define Key Risk Concepts in AI Projects
› 2.2 Identify Common Risks in AI Development
› 2.3 Assess Risk Impact and Probability in AI Contexts
› 2.4 Evaluate Existing Risk Management Frameworks for AI
› 2.5 Develop a Risk Assessment Matrix for AI Projects
› 2.6 Propose Mitigation Strategies for Identified AI Risks
• 3 Developing Mitigation Strategies for AI Risks — 6 classes
› 3.1 Identify Key Risks in AI Projects
› 3.2 Assess the Impact of AI Risks on Stakeholders
› 3.3 Evaluate Current Mitigation Strategies in AI
› 3.4 Develop Tailored Mitigation Plans for Specific Risks
› 3.5 Implement Monitoring Mechanisms for AI Risk Management
› 3.6 Review and Adapt Mitigation Strategies Based on Feedback
• 4 Implementing and Monitoring Risk Management Practices — 6 classes
› 4.1 Identify Key Risks in AI Projects
› 4.2 Develop a Risk Management Framework for AI
› 4.3 Assess the Impact and Likelihood of Risks
› 4.4 Implement Mitigation Strategies for Identified Risks
› 4.5 Monitor and Review Risk Management Practices
› 4.6 Communicate Risk Management Outcomes to Stakeholders
• 5 Communicating and Reporting AI Risks to Stakeholders — 6 classes
› 5.1 Identify Key AI Risk Factors for Stakeholder Communication
› 5.2 Analyze Stakeholder Needs and Expectations in AI Projects
› 5.3 Develop Clear Risk Communication Strategies for AI
› 5.4 Create Effective Risk Reporting Templates for Stakeholders
› 5.5 Demonstrate Best Practices for Presenting AI Risks to Stakeholders
› 5.6 Evaluate Feedback and Improve AI Risk Communications Continuously
LAPT — 85 Great Portland Street, W1W 7LT, London, United Kingdom
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Curriculum current at the date of issue.