Course overview
An AI model can be accurate on average and still treat particular groups unfairly, and that gap is where reputational and legal trouble usually begins. This course looks closely at how bias enters data models and what can be done to find and reduce it.
Sessions move from the sources of bias to detection methods and explainability, staying at a level a non-engineer can follow while respecting the technical detail. Tools and metrics are discussed as subject matter, not operated.
Why a fair-on-average model can still be unfair
Bias rarely announces itself; it hides in training data, in proxy variables, and in feedback loops that quietly reinforce past patterns. Learning to interrogate a model's behavior, rather than trust its accuracy score, is what keeps AI decisions defensible. This course complements AI Ethics and Responsible Data Use with a sharper model-level focus.
What participants will gain
- An understanding of where bias originates in data models.
- Familiarity with methods and metrics used to detect it.
- Awareness of mitigation options and their trade-offs.
- A grasp of explainability and why it supports trust.
Course outline
Unit 1: Introduction to ethical AI
- Why ethics matter in AI systems
- Core principles: fairness, accountability, transparency
- Global ethical standards and frameworks
- Ethical and unethical AI case examples
Unit 2: Sources of bias in AI systems
- Data collection and representation bias
- Algorithmic and design bias
- Feedback loops and unintended consequences
- Real-world biased-outcome examples
Unit 3: Techniques for bias detection and mitigation
- Identifying bias in datasets
- Fairness metrics: demographic parity, equalized odds
- Mitigation across the model lifecycle
- Worked examples with bias-detection tools
Unit 4: Transparency and explainability
- Explainable AI concepts: SHAP and LIME
- Communicating AI decisions to stakeholders
- Accuracy versus interpretability
- Explainable AI case examples
Unit 5: Governance, compliance, and the future of ethical AI
- Governance frameworks for AI ethics
- Aligning with the EU AI Act
- Documenting models for audit and accountability
- The future of fairness and explainability
How the course is delivered
Teaching runs through structured discussion, documented case studies, and worked examples of biased and corrected models. Participants review sample outputs and fairness reports and debate the choices involved, without operating tools or writing code. The aim is informed judgment about model fairness.
Who should attend
- Data, analytics, and AI product professionals seeking an ethics grounding.
- Risk, compliance, and audit staff reviewing AI models.
- Governance and policy specialists working with data teams.
- Managers accountable for AI-driven decisions.
About EuroQuest International Training
EuroQuest International Training has run professional courses since 2015, delivering more than 1,000 titles to over 15,000 participants at venues in Dubai, London, Barcelona, Istanbul, Vienna, Paris, and Geneva. Its courses stay grounded in current practice.
Frequently asked questions
Will I be coding or building models on this course?
No. Detection methods and tools such as SHAP and LIME are discussed as subject matter. The course builds the judgment to question models, not the skills to engineer them, and stays discussion-based.
Do I need a data-science background?
It helps but is not required. Concepts are explained from the ground up, so risk, governance, and management professionals can follow the material fully.
Does the course award a certification?
The course is educational and does not provide certification or a formal qualification. Participants receive practical frameworks and a record of attendance from EuroQuest International Training.
Related courses
- Ethical AI and Responsible Digital Governance
- Machine Learning for Business Intelligence
- Data Governance and Compliance Strategies
- Deep Learning for Advanced Data Analysis
Register for this course
To confirm dates, locations, and registration details, contact EuroQuest International Training or visit the course page on our website.
All Course Dates & Locations
29 dates · 15 cities · Oct 2026 – Jun 2027