5 Key Areas
5 Key Areas of AI ethics, fairness, and explainability are examined in this post, which covers an OTHM Level 7 Diploma unit exploring philosophical challenges, algorithmic bias, and transparent AI decision-making. The content walks through the alignment problem, bias detection and mitigation strategies, and the practical tools used to implement explainable AI solutions in real-world contexts.
Ethics Fairness and Explanation in Artificial Intelligence (F/651/3608) 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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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)