Responsible AI, Privacy and Data Protection

About Course

An accessible course that helps individuals and organisations recognise AI risks, protect information, test outputs, govern use and respond to incidents using practical human-centred controls.

Recognise benefits, limitations and harms across common AI uses.
Apply fairness, privacy, security, transparency and accountability principles.
Protect personal, confidential and organisational information.
Verify AI output and reduce misinformation and over-reliance.
Assess AI use cases by impact and introduce proportionate controls.
Create a responsible AI policy, risk register and implementation plan.

Course Content

Topic 1: Responsible AI Foundations
Understanding AI systems, stakeholders, benefits, limitations and accountability.

Lesson 1.1: AI Systems and Their Boundaries
Lesson 1.2: Benefits, Risks and Trade-offs
Lesson 1.3: Stakeholders and Affected People
Lesson 1.4: Human Accountability
Topic 1 Guided Skills Assignment
Topic 1 Applied Assignment
Topic 1 Knowledge Test

Topic 2: Fairness, Inclusion and Human Impact
Recognising bias, testing outcomes and protecting dignity, access and equal treatment.

Topic 3: Privacy, Confidentiality and Data Protection
Minimising data, using information for clear purposes and preventing unsafe disclosure.

Topic 4: Accuracy, Misinformation and Human Review
Checking facts, calibrating trust and designing review around the consequences of error.

Topic 5: Security and Misuse Prevention
Protecting accounts, prompts, connected systems and users from manipulation or unintended actions.

Topic 6: Transparency, Intellectual Property and Trust
Communicating AI use honestly, respecting creators and enabling questions, correction and appeal.

Topic 7: Governance and Risk Management
Creating policy, inventories, risk tiers, vendor checks and ongoing oversight.

Topic 8: Implementation and Final Capstone
Piloting responsibly, measuring outcomes, managing change and presenting a complete assurance case.

Final Assessment and Course Completion
Piloting responsibly, measuring outcomes, managing change and presenting a complete assurance case.

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About Course

What Will You Learn?

Course Content

Topic 1: Responsible AI Foundations <p>Understanding AI systems, stakeholders, benefits, limitations and accountability.</p>

Lesson 1.1: AI Systems and Their Boundaries

Lesson 1.2: Benefits, Risks and Trade-offs

Lesson 1.3: Stakeholders and Affected People

Lesson 1.4: Human Accountability

Topic 1 Guided Skills Assignment

Topic 1 Applied Assignment

Topic 1 Knowledge Test

Topic 2: Fairness, Inclusion and Human Impact <p>Recognising bias, testing outcomes and protecting dignity, access and equal treatment.</p>

Lesson 2.1: How Bias Enters AI Work

Lesson 2.2: Fairness Is Contextual

Lesson 2.3: Accessibility and Inclusive Design

Lesson 2.4: Impact Assessment and Participation

Topic 2 Guided Skills Assignment

Topic 2 Applied Assignment

Topic 2 Knowledge Test

Topic 3: Privacy, Confidentiality and Data Protection <p>Minimising data, using information for clear purposes and preventing unsafe disclosure.</p>

Lesson 3.1: Personal, Sensitive and Confidential Data

Lesson 3.2: Purpose, Consent and Data Minimisation

Lesson 3.3: Safe Use of Generative AI Tools

Lesson 3.4: Retention, Access and Third Parties

Topic 3 Guided Skills Assignment

Topic 3 Applied Assignment

Topic 3 Knowledge Test

Topic 4: Accuracy, Misinformation and Human Review <p>Checking facts, calibrating trust and designing review around the consequences of error.</p>

Lesson 4.1: Hallucinations and False Confidence

Lesson 4.2: Source Verification and Traceability

Lesson 4.3: Proportionate Human Review

Lesson 4.4: Misinformation, Deepfakes and Provenance

Topic 4 Guided Skills Assignment

Topic 4 Applied Assignment

Topic 4 Knowledge Test

Topic 5: Security and Misuse Prevention <p>Protecting accounts, prompts, connected systems and users from manipulation or unintended actions.</p>

Lesson 5.1: AI Threats in Everyday Use

Lesson 5.2: Prompt Injection and Untrusted Content

Lesson 5.3: Access Control and Least Privilege

Lesson 5.4: Testing, Monitoring and Incident Response

Topic 5 Guided Skills Assignment

Topic 5 Applied Assignment

Topic 5 Knowledge Test

Topic 6: Transparency, Intellectual Property and Trust <p>Communicating AI use honestly, respecting creators and enabling questions, correction and appeal.</p>

Lesson 6.1: When to Disclose AI Use

Lesson 6.2: Explainability and Meaningful Information

Lesson 6.3: Copyright, Ownership and Creator Respect

Lesson 6.4: Challenge, Correction and Appeal

Topic 6 Guided Skills Assignment

Topic 6 Applied Assignment

Topic 6 Knowledge Test

Topic 7: Governance and Risk Management <p>Creating policy, inventories, risk tiers, vendor checks and ongoing oversight.</p>

Lesson 7.1: AI Inventory and Use-Case Approval

Lesson 7.2: Risk Tiers and Proportionate Controls

Lesson 7.3: Policies, Training and Records

Lesson 7.4: Vendor and Procurement Questions

Topic 7 Guided Skills Assignment

Topic 7 Applied Assignment

Topic 7 Knowledge Test

Topic 8: Implementation and Final Capstone <p>Piloting responsibly, measuring outcomes, managing change and presenting a complete assurance case.</p>

Lesson 8.1: Pilot Design and Success Criteria

Lesson 8.2: Monitoring and Continuous Improvement

Lesson 8.3: Change Management and AI Literacy

Lesson 8.4: Building a Responsible AI Assurance Case

Topic 8 Guided Skills Assignment

Topic 8 Applied Assignment

Topic 8 Knowledge Test

Final Assessment and Course Completion <p>Piloting responsibly, measuring outcomes, managing change and presenting a complete assurance case.</p>

Final Revision Guide

Final Capstone Portfolio

Final Examination 20 Questions

Earn a certificate

Student Ratings & Reviews

Topic 1: Responsible AI Foundations
<p>Understanding AI systems, stakeholders, benefits, limitations and accountability.</p>

Topic 2: Fairness, Inclusion and Human Impact
<p>Recognising bias, testing outcomes and protecting dignity, access and equal treatment.</p>

Topic 3: Privacy, Confidentiality and Data Protection
<p>Minimising data, using information for clear purposes and preventing unsafe disclosure.</p>

Topic 4: Accuracy, Misinformation and Human Review
<p>Checking facts, calibrating trust and designing review around the consequences of error.</p>

Topic 5: Security and Misuse Prevention
<p>Protecting accounts, prompts, connected systems and users from manipulation or unintended actions.</p>

Topic 6: Transparency, Intellectual Property and Trust
<p>Communicating AI use honestly, respecting creators and enabling questions, correction and appeal.</p>

Topic 7: Governance and Risk Management
<p>Creating policy, inventories, risk tiers, vendor checks and ongoing oversight.</p>

Topic 8: Implementation and Final Capstone
<p>Piloting responsibly, measuring outcomes, managing change and presenting a complete assurance case.</p>

Final Assessment and Course Completion
<p>Piloting responsibly, measuring outcomes, managing change and presenting a complete assurance case.</p>