Lesson 4, AI Foundations Module
Our final Module 1 session shifted from “can we?” to “should we?”- exploring the ethical responsibilities that come with AI capability. We examined four pillars: Bias (AI learns and amplifies biases from training data, requiring active awareness and diverse prompts), Privacy (never share personal data, credentials, or confidential information; anonymize thoroughly), Integrity (disclose AI use in academic and professional contexts; respect intellectual property), and Accountability (you own all AI-assisted output – never blame the tool).
Through scenario discussions, participants wrestled with real dilemmas: using AI on confidential reports, addressing biased job posting language, disclosing AI assistance to clients, and handling time pressure versus verification responsibility. Rather than providing rigid rules, we guided each participant to create their personal AI ethics framework with commitments around what they’ll always do, never do, when they’ll disclose, how they’ll check for bias, and how they’ll verify outputs.
The message: ethical AI use isn’t about compliance checklists – it’s about thoughtful consideration of impact, responsibility, and integrity in everything you create.
This post is part of the Cambridge AI Lunch & Learn series, a 24-week upskilling programme designed to transform participants from AI curious to AI confident. Through bite-sized
30-minute weekly sessions, we’re building practical AI literacy across Cambridge University Press & Assessment, equipping our team with the skills to work effectively and ethically with artificial intelligence.
Each session follows a proven learn-by-doing approach: 10 minutes of instruction, 10 minutes of demonstration, and 10 minutes of hands-on practice. This isn’t about becoming AI experts but rather it’s about understanding how to augment our work, make better decisions, and navigate the evolving landscape of AI tools with confidence and responsibility.
AI Ethics in Practice
- Introduction to AI & Machine Learning – Technical foundations for ethical use
- AI Capabilities and Limitations – Understanding boundaries for responsible use
- AI Assessment Scale for Schools – Evaluating ethical AI implementation
- Keeping Humans at the Centre – Cambridge approach to ethical AI