This Microcredential deepens awareness of ethical challenges in AI development and integration for learners with foundational AI literacy. It covers algorithmic bias, unfair decision-making, ethical theories (utilitarianism, deontology, virtue ethics), bias mitigation, fairness evaluation, and ethical risk assessment using frameworks like GDPR, the EU AI Act, and Trustworthy AI. Graduates can apply AI responsibly in real contexts and contribute to ethical implementation within organisations.

Self-Assessment Quizzes; Practical Lab Exercises; Assignments; Essays/Reports; Knowledge Checks; Final Exam (Online); Case Studies; Projects

Lectures; Videos; Hands on Labs; Practical Labs; Case Studies; Live Code Demos; Code Reviews; Literature Reading

Analyse advanced ethical theories and AI principles (fairness, accountability, transparency, privacy); assess systems/case studies for bias, privacy and safety; ensure compliance with AI ethics standards and data privacy; recommend ethical practices across the AI lifecycle; communicate ethics issues and solutions to diverse stakeholders.

Understanding of the AI development process; Conducting ethical impact assessments/audits; Applying AI-related regulations and ethical guidelines; Identifying bias/fairness issues in data and model outcomes

Interdisciplinary communication and collaboration; Enhanced problem-solving, decision-making and critical thinking in ethical dilemmas