| 1 | Introduction to AI ethics: concepts and history | [1]p.1–18 |
| 2 | Ethical principles: beneficence, justice, autonomy, explainability | [1]p.19–36 |
| 3 | Fairness and bias: data and algorithmic sources | [3]p.1–28 |
| 4 | Discrimination and equality metrics | [3]p.29–56 |
| 5 | Privacy and data protection | [3]p.57–92 |
| 6 | Transparency and explainable AI (XAI) | [1]p.37–60 |
| 7 | Responsibility and accountability | [2]p.45–80 |
| 8 | Human oversight and safe AI | [2]p.81–120 |
| 9 | AI ethics in healthcare | [2]p.381–410 |
| 10 | AI in public sector and security | [1]p.61–84 |
| 11 | Impacts on education and workforce | [2]p.741–770 |
| 12 | Governance and ethical standards | [2]p.121–160 |
| 13 | Legal frameworks and compliance processes | [2]p.701–740 |
| 14 | Ethical report and project presentations | |