Ethics, Fairness and Explanation in Artificial Intelligence Assignment Brief
| Qualification | OTHM Level 7 Diploma in Artificial Intelligence (610/4802/1) |
| Unit Reference Code | F/651/3608 |
| Unit Name | Ethics, Fairness and Explanation in Artificial Intelligence |
| Credit | 20 |
| GLH | 100 |
| TQT | 200 |
| Mandatory / Optional | Mandatory |
| Unit Grading Type | Pass / Fail |
Assignment Aim
This unit explores the ethical, fairness, and explanatory dimensions of artificial intelligence (AI), which are increasingly critical as AI systems become more integrated into various aspects of society. The module is divided into three main areas: the ethics of AI focusing on philosophical and ethical challenges such as the alignment problem, explainability in Large Language Models (LLMs), and responsibility attribution; fairness and bias in machine learning, examining the concepts of algorithmic fairness, bias detection, and mitigation strategies; and explainable AI (XAI), which addresses the need for transparency in AI decisions to ensure they are justifiable and understandable. By the end of this unit, learners will be equipped to critically engage with these issues, apply fairness measures, and implement explainable AI solutions using practical tools.
Learning Outcomes and Assessment Criteria
| Learning Outcome – The learner will: | Assessment Criteria – The learner can: |
| 1. Understand the ethical implications of developments in AI with respect to underlying philosophical ideas. | 1.1 Explain the key ethical challenges posed by AI developments, including the alignment issues with LLMs. 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. Understand and critique debates on AI safety and AI alignment. | 2.1 Describe the importance of AI safety in the development of AI systems. 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. Be able to detect algorithmic bias in machine learning decisions and measure it based on several common metrics. | 3.1 Identify common sources of bias in machine learning algorithms. 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. Understand algorithmic fairness measures to address bias and perform empirical analysis using appropriate libraries. | 4.1 Explain different approaches to algorithmic fairness. 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. Understand the strengths and weaknesses of different approaches to explanation, and their robustness, in specific instances of AI tasks. | 5.1 Describe the importance of explainability in 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. Be able to implement explanation tasks using widely used Python libraries. | 6.1 Identify appropriate Python libraries for XAI. 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. |
Assessment
To achieve a ‘pass’ for this unit, learners must provide evidence to demonstrate that they have fulfilled all the learning outcomes and meet the standards specified by all assessment criteria.
| Learning Outcomes to be met | Assessment Criteria to be covered | Assessment type | Word count (approx. length) |
| LO1-LO4 | All AC’s under LO1-LO4 | Coursework (Essay) | 3000 words (80%) |
| LO5-LO6 | All AC’s under LO5-LO6 | Coursework (Presentation and Speaker Notes) | 800 words (20%) |
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