AI Ethics, Society & Careers
Fairness, explainability, privacy, safety, regulation, humanitarian AI and your career.
- 01Why AI Ethics Matters: Principles for Responsible EngineersAI systems make or shape decisions about people at scale. We examine real harms, the major principles of responsible AI, why good intentions are not enough, and how ethics becomes concrete engineering practice.
- 02Bias and Fairness in Machine LearningWhat does it mean for a model to be fair, and where does unfairness come from? We examine sources of bias, the failure of "fairness through unawareness", proxy variables, and practical strategies across the ML pipeline.
- 03Fairness Metrics: Demographic Parity, Equalised Odds and CalibrationWe formalise group fairness — demographic parity, equal opportunity, equalised odds, predictive parity and calibration — compute them with Fairlearn, and prove why several cannot hold simultaneously except in special cases.
- 04Explainable AI: LIME, SHAP and Interpretable ModelsWhy did the model decide that? We distinguish interpretable models from post-hoc explanations, derive Shapley values and SHAP, explain LIME, cover global vs local explanations and counterfactuals, and discuss the limits of explanations.
- 05Privacy in Machine Learning and Differential PrivacyModels can leak the data they were trained on. We examine re-identification and model attacks, why anonymisation often fails, the mathematics of differential privacy, DP-SGD, and practical privacy-by-design.
- 06Federated Learning: Training Without Centralising DataFederated learning trains a shared model across many devices or institutions while raw data stays local. We derive FedAvg, discuss non-IID data, communication costs, secure aggregation, privacy limits and real applications.
- 07AI Safety and Alignment: Making Capable Systems Do What We IntendAs AI systems grow more capable, ensuring they pursue intended goals becomes critical. We cover specification gaming, reward hacking, goal misgeneralisation, current alignment techniques, interpretability, evaluations and governance of frontier models.
- 08AI Security: Data Poisoning, Prompt Injection, Model Theft and DefencesAI systems introduce new attack surfaces. We survey threats across the ML lifecycle — data poisoning and backdoors, evasion, model extraction, privacy attacks, prompt injection and supply-chain risks — and practical defences.
- 09AI Regulation and Governance: The EU AI Act and BeyondGovernments and organisations are creating rules for AI. We survey risk-based regulation with the EU AI Act, data-protection law, international principles and standards, and how organisations build AI governance in practice.
- 10AI for Humanitarian Action and Social GoodAI can help humanitarian organisations anticipate crises, map needs and serve people in their own languages — but the stakes and risks are exceptionally high. We survey applications, principles, pitfalls and how students can contribute responsibly.
- 11Deepfakes, Synthetic Media and MisinformationGenerative AI makes realistic fake images, audio and video cheap. We examine how deepfakes are made, the harms they cause, why detection is an arms race, provenance and watermarking solutions, and the role of policy and media literacy.
- 12The Environmental Cost of AI: Energy, Carbon and WaterTraining and running AI models consumes electricity, emits carbon, uses water and requires hardware. We explain how to estimate these costs, what drives them, and practical steps toward efficient, sustainable AI.
- 13AI and the Future of WorkWill AI take our jobs? We look at evidence on automation and augmentation, task-based analysis of exposure, early productivity studies of generative AI, distributional effects, and how individuals, organisations and policy can respond.
- 14Responsible AI in Education: Learning With, Not Instead Of, AIGenerative AI is transforming how students learn and teachers teach. We discuss benefits like personalised tutoring, risks to learning and integrity, why AI-text detectors fail, and practical guidelines for students and educators.
- 15Careers in AI: A Roadmap for StudentsA practical guide to AI careers — the main roles, the skills each needs, a staged learning roadmap, how to build a portfolio that stands out, finding opportunities, and growing throughout your career.
- 16How to Read a Machine Learning Research PaperResearch papers are how new ideas enter the field. We present a three-pass reading method, a guide to each section, questions for critical reading, common red flags in ML papers, and tools for keeping up.
- 17Doing Machine Learning Research: A Guide for Your ThesisA practical guide to undergraduate and master's research in ML — choosing a question, reviewing literature, designing rigorous experiments, avoiding common pitfalls, writing clearly, and conducting research ethically.