AI Ethics Course

Foundations of AI Ethics

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.

Understanding AI Bias

Sources of algorithmic bias (training data, selection, historical).

How bias amplifies through AI systems.

Protected characteristics and discrimination law.

Mitigation strategies and best practices.

Fairness Frameworks

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.

Transparency and Explainability

The 'black box' problem

Levels of transparency required.

Explainability techniques - LIME and SHAP

Legal and ethical requirements for explanation.

Trade-offs: accuracy vs interpretability.

Privacy and Data Protection

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).

Consent and Autonomy

Meaningful consent in AI contexts.

Dark patterns and manipulative design.

Automated decision-making rights (GDPR Article 22).

Designing for informed choice.

Protecting vulnerable populations.

Accountability and Responsibility

Stakeholder responsibilities

Documentation and audit trails.

Human oversight requirements.

Liability and insurance considerations.

Creating effective oversight mechanisms.

Ethical Decision-Making Frameworks

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.

Implementation and Governance

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.

Emerging Challenges

Generative AI and misinformation.

Deepfakes and synthetic media.

AI environmental impact.

Employment displacement and worker rights.

Medical AI and clinical decision support.

Stakeholder Perspectives

Understanding different viewpoints.

Balancing competing interests.

Meaningful stakeholder engagement.

Negotiating ethical requirements.

Building Your Ethical AI Toolkit

Checklists and templates.

Impact assessment forms.

Documentation standards.

Bias detection and explainability tools.

Privacy-preserving techniques.

Resources for continued learning.