Ethics, Fairness and Explanation in Artificial Intelligence (F/651/3608) Assignment Brief 2026

1.2 Critically analyse the alignment problem in AI and its implications, with a focus on the challenges presented by modern LLMs.

1.3 Evaluate the attribution of responsibility in AI systems.

1.4 Critique philosophical debates on AI safety.

2.2 Explain the role of international collaboration in AI safety.

2.3 Critically analyse key arguments in the AI alignment debate.

2.4 Critically evaluate the effectiveness of existing AI safety frameworks.

3.2 Apply metrics to measure bias in AI systems. 3.3 Critically evaluate the impact of bias on AI decision-making processes.

3.4 Develop a strategy to address detected bias in AI systems.

4.2 Critically    analyse           the       trade-offs         between accuracy and fairness in AI models.

4.3 Implement fairness-enhancing techniques in AI models using Python libraries.

4.4 Critically evaluate the effectiveness of fairness interventions in real-world AI systems.

5.2 Explain and compare different approaches to explainability in AI.

5.3 Critically evaluate the robustness of explanation techniques in different AI tasks.

5.4 Implement XAI techniques in a practical AI application.

6.2 Create a simple AI model and apply XAI techniques.

6.3 Critically evaluate the quality of explanations generated by different libraries.

6.4 Justify findings and recommendations based on XAI implementation.