Governance, Risk & Compliance
Data Ethics and AI Governance
This five-day programme prepares professionals to use data and AI in ways their organisations can defend, covering data ethics foundations, bias, fairness and discrimination risk, privacy, consent and lawful data use, AI governance frameworks and structures, and transparency, oversight and accountability. Participants develop skills in bias assessment, AI governance design, oversight and accountability practice, and ethics reporting.
Introduction
Data Ethics and AI Governance Training Course prepares professionals to use data and AI in ways their organisations can defend. Participants work through data ethics principles and ethical reasoning, bias, fairness, and discrimination risk in data and models, privacy, consent, and lawful data use, AI governance frameworks including the NIST AI RMF and ISO/IEC 42001, transparency, explainability, and human oversight design, and responsible AI controls, monitoring, and accountability reporting as one connected discipline. The programme links the analytical power an organisation gains from data and AI with the ethical and governance discipline that keeps its use legitimate. As a result, learners gain a practical command of data ethics and AI governance. Furthermore, the course strengthens ethical judgement, governance design, and control discipline.
Data Ethics and AI Governance Course Objectives
- Explain data ethics principles and ethical reasoning frameworks.
- Apply fairness, bias detection, and discrimination risk assessment.
- Apply privacy, consent, and lawful data use requirements.
- Apply data minimisation, purpose limitation, and retention principles.
- Apply the NIST AI Risk Management Framework and ISO/IEC 42001.
- Apply AI governance structures, policies, and accountability models.
- Apply transparency, explainability, and disclosure practice.
- Apply human oversight, review gates, and override design.
- Apply responsible AI controls, testing, and bias mitigation.
- Apply AI monitoring, incident handling, assurance, and ethics reporting.
Course Methodology
- Interactive sessions built on data ethics and AI governance cases.
- Workshops on bias assessment, governance design, and oversight models.
- Group exercises on ethical dilemmas, explainability, and reporting.
- Hands-on practice with AI registers, control matrices, and ethics checklists.
- Simulation of an AI ethics review and a governance escalation.
- Facilitator coaching and peer review on personal AI governance plans.
Who Should Take This Course
- Data ethics, AI governance, and responsible AI professionals.
- Data protection and privacy officers.
- Data scientists and ML engineers with governance duties.
- Risk, compliance, and internal audit staff covering AI.
- Product and digital managers deploying AI systems.
- Consultants advising on AI ethics and governance frameworks.
Data Ethics and AI Governance Course Outlines
Data Ethics Foundations
- • Data ethics concepts and why they matter to organisations.
- • Ethical reasoning frameworks: consequentialist, deontological, virtue.
- • Data ethics principles: fairness, respect, beneficence, justice.
- • Ethical dilemmas in data collection and use.
- • Power asymmetries, consent, and vulnerable groups.
- • Codes of conduct and professional ethics for data work.
- • Case discussion: a useful dataset collected without legitimate consent.
Bias, Fairness, and Discrimination Risk
- • Sources of bias: historical, sampling, measurement, algorithmic.
- • Protected attributes and proxy variables.
- • Fairness metrics and their trade-offs.
- • Discrimination risk assessment and regulatory exposure.
- • Bias testing methods and dataset auditing.
- • Mitigation strategies: pre-processing, in-processing, post-processing.
- • Workshop: running a bias assessment on a dataset.
Privacy, Consent, and Lawful Data Use
- • Privacy principles and data protection fundamentals.
- • Consent design, transparency notices, and lawful bases.
- • Data minimisation, purpose limitation, and retention.
- • Anonymisation, pseudonymisation, and re-identification risk.
- • Data subject rights and request handling.
- • Data sharing, secondary use, and ethical review.
- • Exercise: assessing a data use case against ethical and legal criteria.
AI Governance Frameworks and Structures
- • AI governance concepts and drivers.
- • The NIST AI Risk Management Framework and its functions.
- • ISO/IEC 42001 AI management system requirements.
- • OECD AI principles and sector regulatory expectations.
- • AI governance structures, councils, and accountability.
- • AI policy, use case intake, and approval gates.
- • Simulation: running an AI ethics and governance review.
Transparency, Oversight, and Accountability
- • Transparency, explainability, and disclosure practice.
- • Model documentation, model cards, and decision records.
- • Human oversight, review gates, and override design.
- • Responsible AI controls, monitoring, and drift detection.
- • AI incident handling and ethics escalation.
- • Assurance, audit, and ethics reporting to boards.
- • Capstone: presenting a data ethics and AI governance framework.
Conclusion
By successfully completing Data Ethics and AI Governance, participants will gain a practical command of data ethics principles, bias and fairness assessment, privacy and lawful data use, AI governance frameworks including the NIST AI RMF and ISO/IEC 42001, and transparency, oversight, assurance and accountability reporting. They will acquire skills in bias assessment, AI governance design, oversight and accountability practice, and ethics reporting that keep data and AI use legitimate and defensible. Gentex Training Center designs the programme to help professionals govern data ethics and AI with confidence.
FAQs for the Data Ethics and AI Governance course
— What is the "Data Ethics and AI Governance" course about?
It prepares professionals to use data and AI in ways their organisations can defend. The course spans data ethics foundations, bias, fairness and discrimination risk, privacy, consent and lawful data use, AI governance frameworks and structures, and transparency, oversight and accountability.
— What are the key benefits of the "Data Ethics and AI Governance" course?
You gain a practical command of data ethics and AI governance that links the analytical power an organisation gains from data and AI with the ethical and governance discipline that keeps its use legitimate. The course improves bias assessment and fairness practice, strengthens privacy and lawful use discipline, sharpens governance design and oversight, and gives you ethics and assurance reporting that stands up to regulators and the public.
— What skills will I gain from the "Data Ethics and AI Governance" course?
You will develop four core skills: data ethics principle application with ethical reasoning, bias, fairness and discrimination risk assessment, AI governance framework application with oversight design, and transparency, accountability and ethics reporting practice.
— What tools, methods, or standards are covered in the "Data Ethics and AI Governance" course?
The course covers data ethics concepts and their organisational importance, ethical reasoning frameworks across consequentialist, deontological and virtue approaches, data ethics principles of fairness, respect, beneficence and justice, ethical dilemmas in data collection and use, power asymmetries, consent and vulnerable groups, codes of conduct and professional ethics for data work, sources of bias across historical, sampling, measurement and algorithmic, protected attributes and proxy variables, fairness metrics and their trade-offs, discrimination risk assessment and regulatory exposure, bias testing methods and dataset auditing, mitigation strategies across pre-processing, in-processing and post-processing, privacy principles and data protection fundamentals, consent design, transparency notices and lawful bases, data minimisation, purpose limitation and retention, anonymisation, pseudonymisation and re-identification risk, data subject rights and request handling, data sharing, secondary use and ethical review, AI governance concepts and drivers, the NIST AI Risk Management Framework and its functions, ISO/IEC 42001 AI management system requirements, OECD AI principles and sector regulatory expectations, AI governance structures, councils and accountability, AI policy, use case intake and approval gates, transparency, explainability and disclosure practice, model documentation, model cards and decision records, human oversight, review gates and override design, responsible AI controls, monitoring and drift detection, AI incident handling and ethics escalation, and assurance, audit and ethics reporting to boards.
— How is the "Data Ethics and AI Governance" course applied in real-world practice?
Participants apply it to real work such as running a bias assessment on a dataset, assessing a data use case against ethical and legal criteria, designing an AI use case intake and approval process, documenting a model with a model card, designing human oversight and override for a deployed system, handling an AI ethics escalation, and presenting an ethics and governance framework to a board. The same methods support privacy impact reviews and responsible AI assurance.
Contact our team and let us help you find the right option.
Introduction
Data Ethics and AI Governance Training Course prepares professionals to use data and AI in ways their organisations can defend. Participants work through data ethics principles and ethical reasoning, bias, fairness, and discrimination risk in data and models, privacy, consent, and lawful data use, AI governance frameworks including the NIST AI RMF and ISO/IEC 42001, transparency, explainability, and human oversight design, and responsible AI controls, monitoring, and accountability reporting as one connected discipline. The programme links the analytical power an organisation gains from data and AI with the ethical and governance discipline that keeps its use legitimate. As a result, learners gain a practical command of data ethics and AI governance. Furthermore, the course strengthens ethical judgement, governance design, and control discipline.
Data Ethics and AI Governance Course Objectives
- Explain data ethics principles and ethical reasoning frameworks.
- Apply fairness, bias detection, and discrimination risk assessment.
- Apply privacy, consent, and lawful data use requirements.
- Apply data minimisation, purpose limitation, and retention principles.
- Apply the NIST AI Risk Management Framework and ISO/IEC 42001.
- Apply AI governance structures, policies, and accountability models.
- Apply transparency, explainability, and disclosure practice.
- Apply human oversight, review gates, and override design.
- Apply responsible AI controls, testing, and bias mitigation.
- Apply AI monitoring, incident handling, assurance, and ethics reporting.
Course Methodology
- Interactive sessions built on data ethics and AI governance cases.
- Workshops on bias assessment, governance design, and oversight models.
- Group exercises on ethical dilemmas, explainability, and reporting.
- Hands-on practice with AI registers, control matrices, and ethics checklists.
- Simulation of an AI ethics review and a governance escalation.
- Facilitator coaching and peer review on personal AI governance plans.
Who Should Take This Course
- Data ethics, AI governance, and responsible AI professionals.
- Data protection and privacy officers.
- Data scientists and ML engineers with governance duties.
- Risk, compliance, and internal audit staff covering AI.
- Product and digital managers deploying AI systems.
- Consultants advising on AI ethics and governance frameworks.
Data Ethics and AI Governance Course Outlines
Data Ethics Foundations
- • Data ethics concepts and why they matter to organisations.
- • Ethical reasoning frameworks: consequentialist, deontological, virtue.
- • Data ethics principles: fairness, respect, beneficence, justice.
- • Ethical dilemmas in data collection and use.
- • Power asymmetries, consent, and vulnerable groups.
- • Codes of conduct and professional ethics for data work.
- • Case discussion: a useful dataset collected without legitimate consent.
Bias, Fairness, and Discrimination Risk
- • Sources of bias: historical, sampling, measurement, algorithmic.
- • Protected attributes and proxy variables.
- • Fairness metrics and their trade-offs.
- • Discrimination risk assessment and regulatory exposure.
- • Bias testing methods and dataset auditing.
- • Mitigation strategies: pre-processing, in-processing, post-processing.
- • Workshop: running a bias assessment on a dataset.
Privacy, Consent, and Lawful Data Use
- • Privacy principles and data protection fundamentals.
- • Consent design, transparency notices, and lawful bases.
- • Data minimisation, purpose limitation, and retention.
- • Anonymisation, pseudonymisation, and re-identification risk.
- • Data subject rights and request handling.
- • Data sharing, secondary use, and ethical review.
- • Exercise: assessing a data use case against ethical and legal criteria.
AI Governance Frameworks and Structures
- • AI governance concepts and drivers.
- • The NIST AI Risk Management Framework and its functions.
- • ISO/IEC 42001 AI management system requirements.
- • OECD AI principles and sector regulatory expectations.
- • AI governance structures, councils, and accountability.
- • AI policy, use case intake, and approval gates.
- • Simulation: running an AI ethics and governance review.
Transparency, Oversight, and Accountability
- • Transparency, explainability, and disclosure practice.
- • Model documentation, model cards, and decision records.
- • Human oversight, review gates, and override design.
- • Responsible AI controls, monitoring, and drift detection.
- • AI incident handling and ethics escalation.
- • Assurance, audit, and ethics reporting to boards.
- • Capstone: presenting a data ethics and AI governance framework.
Conclusion
By successfully completing Data Ethics and AI Governance, participants will gain a practical command of data ethics principles, bias and fairness assessment, privacy and lawful data use, AI governance frameworks including the NIST AI RMF and ISO/IEC 42001, and transparency, oversight, assurance and accountability reporting. They will acquire skills in bias assessment, AI governance design, oversight and accountability practice, and ethics reporting that keep data and AI use legitimate and defensible. Gentex Training Center designs the programme to help professionals govern data ethics and AI with confidence.
FAQs for the Data Ethics and AI Governance course
— What is the "Data Ethics and AI Governance" course about?
It prepares professionals to use data and AI in ways their organisations can defend. The course spans data ethics foundations, bias, fairness and discrimination risk, privacy, consent and lawful data use, AI governance frameworks and structures, and transparency, oversight and accountability.
— What are the key benefits of the "Data Ethics and AI Governance" course?
You gain a practical command of data ethics and AI governance that links the analytical power an organisation gains from data and AI with the ethical and governance discipline that keeps its use legitimate. The course improves bias assessment and fairness practice, strengthens privacy and lawful use discipline, sharpens governance design and oversight, and gives you ethics and assurance reporting that stands up to regulators and the public.
— What skills will I gain from the "Data Ethics and AI Governance" course?
You will develop four core skills: data ethics principle application with ethical reasoning, bias, fairness and discrimination risk assessment, AI governance framework application with oversight design, and transparency, accountability and ethics reporting practice.
— What tools, methods, or standards are covered in the "Data Ethics and AI Governance" course?
The course covers data ethics concepts and their organisational importance, ethical reasoning frameworks across consequentialist, deontological and virtue approaches, data ethics principles of fairness, respect, beneficence and justice, ethical dilemmas in data collection and use, power asymmetries, consent and vulnerable groups, codes of conduct and professional ethics for data work, sources of bias across historical, sampling, measurement and algorithmic, protected attributes and proxy variables, fairness metrics and their trade-offs, discrimination risk assessment and regulatory exposure, bias testing methods and dataset auditing, mitigation strategies across pre-processing, in-processing and post-processing, privacy principles and data protection fundamentals, consent design, transparency notices and lawful bases, data minimisation, purpose limitation and retention, anonymisation, pseudonymisation and re-identification risk, data subject rights and request handling, data sharing, secondary use and ethical review, AI governance concepts and drivers, the NIST AI Risk Management Framework and its functions, ISO/IEC 42001 AI management system requirements, OECD AI principles and sector regulatory expectations, AI governance structures, councils and accountability, AI policy, use case intake and approval gates, transparency, explainability and disclosure practice, model documentation, model cards and decision records, human oversight, review gates and override design, responsible AI controls, monitoring and drift detection, AI incident handling and ethics escalation, and assurance, audit and ethics reporting to boards.
— How is the "Data Ethics and AI Governance" course applied in real-world practice?
Participants apply it to real work such as running a bias assessment on a dataset, assessing a data use case against ethical and legal criteria, designing an AI use case intake and approval process, documenting a model with a model card, designing human oversight and override for a deployed system, handling an AI ethics escalation, and presenting an ethics and governance framework to a board. The same methods support privacy impact reviews and responsible AI assurance.
Partners in Learning
Our Partners in Learning include leading companies and organizations that trust Gentex Training Center for high-quality training courses and professional development programs. Together, we deliver impactful training programs that support skills growth, leadership development, and organizational excellence.
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