AI Ethics
Identify and assess ethical risks in AI systems
Recognise bias, fairness and transparency issues
Conduct privacy and fairness assessments
Navigate accountability questions with confidence
Implement ethical safeguardsIf attending an online course you must have a licensed copy of any software required and access to a suitable computer.
You will need access to one paid AI tool such as ChatGPT, Copilot, or Claude.
Course leader is an experienced industry expert
What is AI Ethics and why it matters now.
Core ethical principles for AI.
Regulatory landscape (EU AI Act, UK frameworks).
Real-world case studies of ethical failures.
Sources of algorithmic bias (training data, selection, historical).
How bias amplifies through AI systems.
Protected characteristics and discrimination law.
Mitigation strategies and best practices.
Different definitions of fairness.
Individual vs group fairness.
Demographic parity, equal opportunity, predictive parity.
Trade-offs between fairness metrics.
Choosing appropriate criteria for your context.
The 'black box' problem
Levels of transparency required.
Explainability techniques - LIME and SHAP
Legal and ethical requirements for explanation.
Trade-offs: accuracy vs interpretability.
GDPR and UK data protection fundamentals.
Lawful basis for processing and data minimisation.
Privacy by design principles.
AI-specific risks (re-identification, inference attacks).
Individual rights (access, rectification, erasure).
Meaningful consent in AI contexts.
Dark patterns and manipulative design.
Automated decision-making rights (GDPR Article 22).
Designing for informed choice.
Protecting vulnerable populations.
Stakeholder responsibilities
Documentation and audit trails.
Human oversight requirements.
Liability and insurance considerations.
Creating effective oversight mechanisms.
IEEE Ethically Aligned Design
EU Ethics Guidelines for Trustworthy AI.
UK Government's AI ethics framework.
Practical application steps.
Case study analysis and peer review.
Building an ethical AI culture.
Establishing governance structures (ethics committees, review boards, etc).
Creating policies and procedures.
Red flags: when to pause or stop.
Continuous monitoring and evaluation.
Generative AI and misinformation.
Deepfakes and synthetic media.
AI environmental impact.
Employment displacement and worker rights.
Medical AI and clinical decision support.
Understanding different viewpoints.
Balancing competing interests.
Meaningful stakeholder engagement.
Negotiating ethical requirements.
Checklists and templates.
Impact assessment forms.
Documentation standards.
Bias detection and explainability tools.
Privacy-preserving techniques.
Resources for continued learning.
Professionals who wish to develop skills for their current role and companies who believe in developing their talent.
People seeking opportunities which require new skills. Industry relevant training for those pursuing a new role.
Certified courses for those who have earned their theoretical stripes, but need to prove capability to future employers.
Learn face-to-face with expert mentors in a dynamic setting that inspires growth and confidence.
Join remotely with expert mentors in real time — flexible, interactive learning that fits your lifestyle.
Upskill your team together. Build your team and develop consistency, and collective confidence.
Define your direction with purpose, aligning your learning journey to personal and career goals.
Gain practical insight from real-world experts who bring clarity, confidence, and momentum to your growth.
Your certificate of attendance is proof of progress — benchmarking your skills and commitment to growth.
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