AI Ethics and Explainability

As artificial intelligence continues to influence decision-making in healthcare, finance, justice, and beyond, questions around fairness, accountability, and transparency have moved to the forefront of AI development. This course introduces the ethical challenges posed by AI systems and focuses on explainable AI (XAI) as a critical component for trust, compliance, and responsible innovation.

Participants will explore the intersection of ethics, regulation, and technical solutions designed to make AI systems understandable and fair. The course offers a balance of conceptual foundations, real-world case studies, and practical tools to assess and improve the transparency of AI models—especially in high-stakes or regulated environments.

Section 1: Foundations of AI Ethics

• Why AI ethics matters: trust, safety, and social responsibility.
• Key ethical principles: fairness, accountability, transparency, autonomy.
• Common risks: bias, discrimination, opacity, surveillance.
• Real-world case studies: when AI fails or harms.
• Ethical frameworks: consequentialism, deontology, virtue ethics in AI.
• Stakeholder impact and ethical design considerations.
• Role of human oversight and moral reasoning.

Section 2: Understanding Bias and Fairness in AI

• How bias enters AI systems (data, design, deployment).
• Types of bias: historical, algorithmic, selection, measurement.
• Evaluating fairness: individual vs. group fairness metrics.
• Tools for bias detection and mitigation (Fairlearn, AI Fairness 360, etc.).
• Fairness trade-offs: performance vs. equity
• Inclusive dataset design and demographic parity.
• Guidelines for equitable model development.

Section 3: Explainable AI (XAI) Principles and Techniques

• What is XAI and why it matters.
• Black-box vs. white-box models: strengths and limitations.
• Post-hoc explanation methods: SHAP, LIME, counterfactuals.
• Interpretable models: decision trees, rule-based systems, linear models.
• Domain-specific challenges in explainability (e.g., healthcare, finance).
• Communicating AI decisions to non-technical stakeholders.
• Hands-on walkthrough: explaining a black-box classifier.

Section 4: Governance, Compliance, and Regulation

• Overview of global AI regulations and guidelines (EU AI Act, OECD, UNESCO).
• Industry standards: ISO/IEC 24028, NIST AI Risk Management Framework.
• Ethics review boards, model documentation, and audit trails.
• AI in high-risk sectors: health, law, public services.
• Data protection, consent, and the right to explanation.
• Building internal governance structures for ethical AI.
• Preparing for regulatory audits and external evaluations.

Section 5: Designing Ethical and Transparent AI Systems

• Ethical design thinking in AI product development.
• Balancing accuracy, explainability, and user trust.
• Human-in-the-loop systems and oversight protocols.
• Transparency by design: UI, feedback, documentation.
• Building ethical AI cultures inside organizations.
• Scenario planning for ethical decision-making.
• Action planning: applying XAI and ethics to your projects.

Upon successful completion of this training course, delegates will be awarded a Holistique Training Certificate of Completion. For those who attend and complete the online training course, a Holistique Training e-Certificate will be provided.

Holistique Training Certificates are accredited by the British Accreditation Council (BAC) and The CPD Certification Service (CPD), and are certified under ISO 9001, ISO 21001, and ISO 29993 standards.

CPD credits for this course are granted by our Certificates and will be reflected on the Holistique Training Certificate of Completion. In accordance with the standards of The CPD Certification Service, one CPD credit is awarded per hour of course attendance. A maximum of 50 CPD credits can be claimed for any single course we currently offer.