Responsible Use of AI

Responsible Use of AI

AI tools like GitHub Copilot, ChatGPT, and Claude are now part of everyday development work, but using them well is a matter of personal judgement, not just skill. This subject covers the responsibility side of AI use: academic integrity (attribution, not outsourcing your thinking), critically evaluating AI output for hallucination and bias, and the legal risks around copyright, GDPR, and the EU AI Act. It's the personal-ethics counterpart to Working with AI in Designing & Realising, which covers prompting effectiveness and developer productivity — this subject is about accountability and judgement, not technique.

Starting Points

• LinkedIn Learning. Using Generative AI Ethically at Work (Katrina Ingram, 1h 9m).
• LinkedIn Learning. Ethics in the Age of Generative AI (Vilas Dhar, 39m).
• European Commission. (2022). Ethical guidelines on the use of AI and data in teaching and learning.
• UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence.
• LeMaire, C., & Abshire, B. (2025). AI for Everyday IT: Accelerate Workplace Productivity. Manning.

Key Points

• You explain in your own words what academic integrity means when using generative AI tools, including when use is allowed and how to attribute AI assistance.
• You critically evaluate an AI-generated output (code, text, or design suggestion) for correctness, bias, and hidden assumptions before using it.
• You describe the key legal risks of using AI tools at work (copyright of AI-generated content, GDPR data residency, organisational policies) with a concrete example.
• You reflect on how your own use of AI during the studio semester affected your learning: what you learned more deeply, and what risks of outsourcing thinking you experienced.
• You apply at least one organisational guideline or framework for responsible AI use in your project documentation (e.g. noting where and how AI was used).