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Master Certificate Level 6-7 Leadership ISO IT & Related Technologies Artificial Intelligence

ISO 24028 — Overview of Trustworthiness in AI

ISO Certification Programme

6 Subjects
30 Chapters
150 Lessons
500 Marks

LAPT — London Academy of Professional Training

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ISO 24028 — Overview of Trustworthiness in AI
Master Certificate Level 6-7
  • IIT-AII-24028
  • Leadership Stage
  • 500 total marks
  • Pass: 325 marks (65%)
  • Validity: Lifetime
Enrol Now View Brochure
AwardMaster Certificate
Global LevelLevel 6-7
Total Marks500
Pass Mark325 (65%)
Subjects6
Chapters30
Classes150

About This Certification

Who Is This For?

This certification is designed for senior professionals, including AI managers, compliance officers, and technology leaders with a minimum of five years' experience in AI or related fields. Individuals seeking this certification need to understand trustworthiness in AI to enhance their organisational strategies and ensure ethical practices.

Course Curriculum

6 subjects • 30 chapters • 150 classes
01
Case Studies in Trustworthy AI Deployment
5 chapters • 50 marks • 15h
Understanding Trustworthiness in AI: Principles and Frameworks
Assessing AI Systems: Metrics for Trustworthiness
Case Studies: Successful Deployment of Trustworthy AI
Challenges in Maintaining AI Trustworthiness in Deployment
Future Trends and Innovations in Trustworthy AI
02
Leadership and Management in AI Initiatives
5 chapters • 30 classes • 75 marks • 20h
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
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
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
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
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
03
Risk Management in AI Projects
5 chapters • 30 classes • 100 marks • 25h
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
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
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
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
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
04
Governance Frameworks for AI
5 chapters • 30 classes • 100 marks • 30h
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
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
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
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
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
05
Ethical and Legal Aspects of AI
5 chapters • 30 classes • 75 marks • 30h
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
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
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
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
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
06
Foundations of Trustworthiness in AI
5 chapters • 30 classes • 100 marks • 40h
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
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
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
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
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

Assessment & Grading

Assessment Methods
  • Written Examination
  • Practical Assignment
  • Portfolio Assessment
Theory
50%
Practical
35%
Project
15%
ISO 24028 — Overview of Trustworthiness in AI
Master Certificate Level 6-7
Enrol Now View Brochure
Enrol Now

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