AI Ethics, Responsible AI and Governance

Understanding AI and Why Responsible Use Matters

• Reviewing how AI tools work at a practical level: what they can and cannot do and why outputs vary
• Understanding the difference between narrow AI, generative AI and emerging AI systems in a professional context
• Recognising why AI ethics and governance have become business-critical concerns for organisations of all sizes
• Exploring real-world examples of AI failures, unintended consequences and reputational harm caused by unethical AI use
• Establishing a shared vocabulary for responsible AI: fairness, transparency, accountability, privacy and human oversight

Understanding Bias in AI and How to Recognise It

• Understanding how bias enters AI systems through training data, model design and human decision-making
• Recognising the different types of bias relevant to professional contexts: historical, representation, measurement and feedback bias
• Exploring documented examples of AI bias in recruitment, lending, healthcare and content moderation
• Understanding the difference between intentional and unintentional bias and why the latter is often harder to detect
• Developing practical strategies for identifying and reducing bias in AI-generated outputs used in professional workflows

Data Privacy and Security When Using AI

• Understanding what happens to data when it is entered into AI tools: storage, training use and third-party access
• Reviewing the data privacy implications of using consumer AI tools such as ChatGPT, Claude and Gemini with business information
• Understanding GDPR obligations and how they apply to the use of AI in processing personal data
• Identifying which types of information should never be entered into a public AI tool: personal data, client data and commercially sensitive content
• Reviewing best practice for using AI tools securely within an organisational context, including enterprise-tier AI products

Transparency and Honesty in AI-Generated Content

• Understanding the obligation to be transparent about AI involvement in content, decisions and communications
• Reviewing emerging sector guidelines and regulatory expectations around disclosure of AI-generated material
• Exploring the risks of passing off AI-generated content as entirely human-produced in professional and public-facing contexts
• Understanding hallucination: why AI tools sometimes produce plausible but factually incorrect outputs
• Developing a personal and organisational approach to reviewing, verifying and responsibly attributing AI-generated content

AI and Intellectual Property

• Understanding the intellectual property landscape as it applies to AI-generated text, images, code and other content
• Reviewing the current legal position on copyright ownership of AI-generated outputs in the UK and internationally
• Understanding the risks of AI tools reproducing copyrighted material and how to mitigate them in professional workflows
• Exploring the implications of using proprietary data, brand assets or third-party content as inputs to AI tools
• Keeping up to date with rapidly evolving AI and intellectual property legislation and guidance

Human Oversight and Accountability in AI Use

• Understanding why human oversight is essential when AI is used to inform or automate consequential decisions
• Exploring the concept of meaningful human control: what it means in practice and how to maintain it
• Reviewing high-risk AI use cases where automated decision-making requires particular caution: hiring, credit, health and legal contexts
• Understanding who is accountable when AI-generated content or decisions cause harm within an organisation
• Developing clear internal processes for escalation, review and sign-off when AI is used in sensitive or high-stakes situations

AI Governance Frameworks and Organisational Policy

• Reviewing major AI governance frameworks including the EU AI Act, the UK AI regulatory approach and ISO 42001
• Understanding how organisations are developing internal AI policies, acceptable use guidelines and governance structures
• Exploring the key components of an effective organisational AI policy: scope, permitted tools, prohibited uses and review processes
• Understanding the role of AI risk assessment in identifying and managing the ethical risks of new AI implementations
• Reviewing practical examples of AI governance in action across different sectors: financial services, healthcare, media and public bodies

Building a Responsible Approach to AI in Your Organisation

• Identifying the most significant AI ethics risks relevant to your own professional role and industry sector
• Developing practical guidelines for responsible AI use within your team or organisation
• Understanding how to evaluate a new AI tool for ethical risk before adopting it in a professional workflow
• Staying informed about AI ethics developments: key organisations, publications and regulatory bodies to follow
• Creating a personal action plan for embedding responsible AI principles into everyday professional practice