Regulatory Compliance
Artificial Intelligence (AI) Ethics, Regulations & Compliance
This five-day programme prepares professionals to deploy AI responsibly and prove it, covering AI ethics principles, the EU AI Act risk tiers, data protection duties, bias and fairness testing, and AI governance controls. Participants develop skills in impact assessment, model documentation, oversight design, and audit readiness.
Introduction
Artificial Intelligence (AI) Ethics, Regulations & Compliance Training Course prepares professionals to deploy AI responsibly and prove it. Participants work through ethical principles, the EU AI Act risk tiers, data protection duties, model governance, algorithmic bias testing, transparency and explainability, and audit evidence as one connected framework. The programme links the rules AI systems must satisfy with the controls and documentation that demonstrate compliance. As a result, learners gain a practical method for governing AI. Furthermore, the course strengthens risk classification, impact assessment, and oversight design.
Artificial Intelligence (AI) Ethics, Regulations & Compliance Course Objectives
- Explain AI ethics principles: fairness, accountability, and human oversight.
- Apply the EU AI Act risk tiers: prohibited, high-risk, limited, and minimal.
- Identify obligations for providers, deployers, importers, and distributors.
- Apply GDPR principles to AI systems and automated decision-making.
- Conduct a fundamental rights impact assessment (FRIA) and DPIA.
- Detect and mitigate algorithmic bias using fairness metrics.
- Apply transparency, explainability, and disclosure requirements.
- Build AI governance: inventory, risk register, and model documentation.
- Apply human oversight, monitoring, and incident reporting controls.
- Apply conformity assessment, technical documentation, and audit readiness.
Course Methodology
- Interactive sessions built on AI ethics and compliance cases.
- Workshops on risk classification, impact assessment, and governance design.
- Group exercises on bias testing, transparency notices, and oversight design.
- Hands-on practice with AI inventories, risk registers, and model cards.
- Simulation of an AI compliance review and a regulatory audit.
- Facilitator coaching and peer review on personal AI governance plans.
Who Should Take This Course
- Compliance, risk, and governance professionals.
- Data protection officers and privacy specialists.
- AI and machine learning product owners and managers.
- Data scientists and model developers working on governed systems.
- Internal auditors covering AI, data, and technology controls.
- Legal advisors, policy staff, and consultants on AI regulation.
Artificial Intelligence (AI) Ethics, Regulations & Compliance Course Outlines
AI Ethics Principles and Responsible AI
- • Core AI ethics principles and their practical meaning.
- • Fairness, non-discrimination, and equity concepts.
- • Accountability, human oversight, and human-in-the-loop design.
- • Transparency, explainability, and interpretability basics.
- • Ethical frameworks: OECD AI Principles and UNESCO recommendations.
- • Responsible AI charters and organisational commitments.
- • Case discussion: an AI system that produced unfair outcomes.
The EU AI Act and Global Regulation
- • The EU AI Act structure and risk-based approach.
- • Prohibited practices and unacceptable risk use cases.
- • High-risk classification and the Annex III list.
- • Obligations for providers, deployers, importers, and distributors.
- • General-purpose AI models and transparency obligations.
- • Other regimes: OECD guidance and national AI strategies.
- • Workshop: classifying an AI use case under the AI Act.
Data Protection, Privacy, and Automated Decisions
- • GDPR principles applied to AI training and inference data.
- • Lawful bases, purpose limitation, and data minimisation.
- • Automated decision-making and Article 22 rights.
- • Data protection impact assessment (DPIA) for AI systems.
- • Data governance, provenance, and quality requirements.
- • Cross-border data transfers and vendor obligations.
- • Exercise: completing a DPIA for an AI system.
Bias, Fairness, and Model Governance
- • Sources of bias in data, design, and deployment.
- • Fairness metrics and group vs individual fairness.
- • Bias testing methods and evaluation datasets.
- • Model documentation: model cards and datasheets.
- • Model risk management and validation practice.
- • Monitoring, drift detection, and performance review.
- • Simulation: running a bias test and documenting findings.
Compliance Controls, Oversight, and Audit
- • AI governance structure and accountability assignment.
- • AI inventory, risk register, and lifecycle stage gates.
- • Human oversight design and override procedures.
- • Transparency notices, disclosures, and user information.
- • Incident reporting, logging, and record keeping duties.
- • Conformity assessment, technical documentation, and audit readiness.
- • Capstone: presenting an AI ethics and compliance programme.
Conclusion
By successfully completing Artificial Intelligence (AI) Ethics, Regulations & Compliance, participants will gain a practical command of AI ethics principles, EU AI Act risk classification, data protection duties, bias and fairness testing, transparency, and AI governance controls. They will acquire skills in impact assessment, model documentation, oversight design, and audit readiness that support responsible AI deployment. Gentex Training Center designs the programme to help professionals govern AI with confidence.
FAQs
— What is the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course about?
It prepares professionals to deploy AI responsibly and prove it. The course spans AI ethics principles and responsible AI, the EU AI Act and global regulation, data protection and automated decisions, bias, fairness and model governance, and compliance controls, oversight and audit.
— What are the key benefits of the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course?
You gain a practical method for governing AI that connects rules with demonstrable controls. The course improves risk classification and impact assessment, strengthens bias and fairness testing, sharpens transparency and oversight design, and gives you documentation and audit readiness that withstand regulatory scrutiny.
— What skills will I gain from the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course?
You will develop four core skills: applying AI ethics principles and the EU AI Act risk classification, data protection and impact assessment for AI systems, algorithmic bias detection and fairness testing with model documentation, and AI governance design with oversight controls and audit readiness.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course?
The course covers AI ethics principles for fairness, accountability and human oversight, the OECD AI Principles and UNESCO recommendations, the EU AI Act risk-based approach and prohibited practices and Annex III high-risk list, provider, deployer, importer and distributor obligations, general-purpose AI model transparency obligations, GDPR principles for AI training and inference data, lawful bases and purpose limitation and data minimisation, automated decision-making and Article 22 rights, data protection impact assessment (DPIA), fundamental rights impact assessment (FRIA), data governance and provenance and quality requirements, cross-border data transfers and vendor obligations, sources of bias in data, design and deployment, fairness metrics and group versus individual fairness, bias testing methods and evaluation datasets, model cards and datasheets, model risk management and validation, drift detection and performance monitoring, AI governance structures and accountability assignment, AI inventory and risk register and lifecycle stage gates, human oversight design and override procedures, transparency notices and disclosures, incident reporting and logging and record keeping, and conformity assessment and technical documentation.
— How is the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course applied in real-world practice?
Participants apply it to real work such as classifying an AI use case under the EU AI Act, completing a DPIA for an AI system, running a bias test and documenting the findings, building an AI inventory and risk register, drafting transparency notices and user information, and preparing technical documentation for an AI compliance review. The same methods support incident reporting and regulatory audit preparation.
Contact our team and let us help you find the right option.
Introduction
Artificial Intelligence (AI) Ethics, Regulations & Compliance Training Course prepares professionals to deploy AI responsibly and prove it. Participants work through ethical principles, the EU AI Act risk tiers, data protection duties, model governance, algorithmic bias testing, transparency and explainability, and audit evidence as one connected framework. The programme links the rules AI systems must satisfy with the controls and documentation that demonstrate compliance. As a result, learners gain a practical method for governing AI. Furthermore, the course strengthens risk classification, impact assessment, and oversight design.
Artificial Intelligence (AI) Ethics, Regulations & Compliance Course Objectives
- Explain AI ethics principles: fairness, accountability, and human oversight.
- Apply the EU AI Act risk tiers: prohibited, high-risk, limited, and minimal.
- Identify obligations for providers, deployers, importers, and distributors.
- Apply GDPR principles to AI systems and automated decision-making.
- Conduct a fundamental rights impact assessment (FRIA) and DPIA.
- Detect and mitigate algorithmic bias using fairness metrics.
- Apply transparency, explainability, and disclosure requirements.
- Build AI governance: inventory, risk register, and model documentation.
- Apply human oversight, monitoring, and incident reporting controls.
- Apply conformity assessment, technical documentation, and audit readiness.
Course Methodology
- Interactive sessions built on AI ethics and compliance cases.
- Workshops on risk classification, impact assessment, and governance design.
- Group exercises on bias testing, transparency notices, and oversight design.
- Hands-on practice with AI inventories, risk registers, and model cards.
- Simulation of an AI compliance review and a regulatory audit.
- Facilitator coaching and peer review on personal AI governance plans.
Who Should Take This Course
- Compliance, risk, and governance professionals.
- Data protection officers and privacy specialists.
- AI and machine learning product owners and managers.
- Data scientists and model developers working on governed systems.
- Internal auditors covering AI, data, and technology controls.
- Legal advisors, policy staff, and consultants on AI regulation.
Artificial Intelligence (AI) Ethics, Regulations & Compliance Course Outlines
AI Ethics Principles and Responsible AI
- • Core AI ethics principles and their practical meaning.
- • Fairness, non-discrimination, and equity concepts.
- • Accountability, human oversight, and human-in-the-loop design.
- • Transparency, explainability, and interpretability basics.
- • Ethical frameworks: OECD AI Principles and UNESCO recommendations.
- • Responsible AI charters and organisational commitments.
- • Case discussion: an AI system that produced unfair outcomes.
The EU AI Act and Global Regulation
- • The EU AI Act structure and risk-based approach.
- • Prohibited practices and unacceptable risk use cases.
- • High-risk classification and the Annex III list.
- • Obligations for providers, deployers, importers, and distributors.
- • General-purpose AI models and transparency obligations.
- • Other regimes: OECD guidance and national AI strategies.
- • Workshop: classifying an AI use case under the AI Act.
Data Protection, Privacy, and Automated Decisions
- • GDPR principles applied to AI training and inference data.
- • Lawful bases, purpose limitation, and data minimisation.
- • Automated decision-making and Article 22 rights.
- • Data protection impact assessment (DPIA) for AI systems.
- • Data governance, provenance, and quality requirements.
- • Cross-border data transfers and vendor obligations.
- • Exercise: completing a DPIA for an AI system.
Bias, Fairness, and Model Governance
- • Sources of bias in data, design, and deployment.
- • Fairness metrics and group vs individual fairness.
- • Bias testing methods and evaluation datasets.
- • Model documentation: model cards and datasheets.
- • Model risk management and validation practice.
- • Monitoring, drift detection, and performance review.
- • Simulation: running a bias test and documenting findings.
Compliance Controls, Oversight, and Audit
- • AI governance structure and accountability assignment.
- • AI inventory, risk register, and lifecycle stage gates.
- • Human oversight design and override procedures.
- • Transparency notices, disclosures, and user information.
- • Incident reporting, logging, and record keeping duties.
- • Conformity assessment, technical documentation, and audit readiness.
- • Capstone: presenting an AI ethics and compliance programme.
Conclusion
By successfully completing Artificial Intelligence (AI) Ethics, Regulations & Compliance, participants will gain a practical command of AI ethics principles, EU AI Act risk classification, data protection duties, bias and fairness testing, transparency, and AI governance controls. They will acquire skills in impact assessment, model documentation, oversight design, and audit readiness that support responsible AI deployment. Gentex Training Center designs the programme to help professionals govern AI with confidence.
FAQs
— What is the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course about?
It prepares professionals to deploy AI responsibly and prove it. The course spans AI ethics principles and responsible AI, the EU AI Act and global regulation, data protection and automated decisions, bias, fairness and model governance, and compliance controls, oversight and audit.
— What are the key benefits of the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course?
You gain a practical method for governing AI that connects rules with demonstrable controls. The course improves risk classification and impact assessment, strengthens bias and fairness testing, sharpens transparency and oversight design, and gives you documentation and audit readiness that withstand regulatory scrutiny.
— What skills will I gain from the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course?
You will develop four core skills: applying AI ethics principles and the EU AI Act risk classification, data protection and impact assessment for AI systems, algorithmic bias detection and fairness testing with model documentation, and AI governance design with oversight controls and audit readiness.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course?
The course covers AI ethics principles for fairness, accountability and human oversight, the OECD AI Principles and UNESCO recommendations, the EU AI Act risk-based approach and prohibited practices and Annex III high-risk list, provider, deployer, importer and distributor obligations, general-purpose AI model transparency obligations, GDPR principles for AI training and inference data, lawful bases and purpose limitation and data minimisation, automated decision-making and Article 22 rights, data protection impact assessment (DPIA), fundamental rights impact assessment (FRIA), data governance and provenance and quality requirements, cross-border data transfers and vendor obligations, sources of bias in data, design and deployment, fairness metrics and group versus individual fairness, bias testing methods and evaluation datasets, model cards and datasheets, model risk management and validation, drift detection and performance monitoring, AI governance structures and accountability assignment, AI inventory and risk register and lifecycle stage gates, human oversight design and override procedures, transparency notices and disclosures, incident reporting and logging and record keeping, and conformity assessment and technical documentation.
— How is the "Artificial Intelligence (AI) Ethics, Regulations & Compliance" course applied in real-world practice?
Participants apply it to real work such as classifying an AI use case under the EU AI Act, completing a DPIA for an AI system, running a bias test and documenting the findings, building an AI inventory and risk register, drafting transparency notices and user information, and preparing technical documentation for an AI compliance review. The same methods support incident reporting and regulatory audit preparation.
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