Using AI tools to generate layouts, copy, or code is now a normal part of interface work, but it raises questions that a tool cannot answer for you. Who gets credit for a design that an assistant largely produced? What happens when training data encodes stereotypes that surface in generated images, personas, or default settings? Can you legally ship code or assets that a model reproduced from someone else's work? This topic looks at those questions and at the professional obligations that come with them: being transparent about how work was produced, checking outputs for bias and misrepresentation, respecting licensing, keeping your own skills sharp, and deploying AI responsibly in products real people will use.
These concerns are not abstract for HCI practitioners. The designer is usually the last human checkpoint between a model's output and a user's screen, so judgments about attribution, fairness, and appropriateness land on you. Professional codes in computing treat this as a duty of competence, not a matter of taste: you are responsible for what you ship, regardless of which tool produced the first draft.
The three talks below approach the problem from different angles — present-day harms and bias, the data behind generative models and the creators it draws from, and the effect of heavy AI use on your own thinking. While watching, keep notes on concrete practices you could adopt: what you would disclose on a project, what you would check before accepting a generated interface element, and where you would deliberately do the work yourself.
AI ethics researcher Sasha Luccioni argues that speculative existential risk distracts from harms AI systems are causing right now, and points to issues such as bias in model outputs, environmental cost, and the use of copyrighted material in training data. The talk describes work on tools that let people examine and measure these problems rather than take vendor claims on faith.
It directly supports the bias and representation subtopic and gives students a vocabulary for discussing present-day, measurable harms; the TED platform and the speaker's research background make it a credible, accessible starting point.
Ed Newton-Rex, who worked in generative AI before resigning over training-data practices, examines how models are built from people, compute, and data, and why the creative work in that third category is often used without consent, credit, or compensation. The talk lays out the argument over whether training on copyrighted material is fair and discusses what could be done differently.
This is the clearest fit for the intellectual property and licensing subtopic, and the speaker's insider perspective helps students see why attribution and provenance matter in professional practice rather than only in legal filings.
Advait Sarkar looks at what happens to human reasoning when we routinely hand tasks to chatbots, and the trade-off between working faster and thinking less. The talk suggests ways to use AI that preserve rather than erode judgment and critical engagement with the work.
It speaks squarely to maintaining professional competence, and it gives students a practical frame for deciding when to use an assistant and when deferring to one undermines the evaluation skills this course is building.
Together these talks round out the module's picture of AI in HCI workflows. The first two deepen MO1 by showing what these tools actually are — systems built on specific data, with specific costs and biases — which is context students need before describing their role in design work. All three feed MO3 most directly: Luccioni gives students bias and representation criteria to test generated interfaces against, Newton-Rex supplies the provenance and licensing questions to ask about generated assets, and Sarkar argues for the kind of active critical review that keeps evaluation meaningful. The result is that when students apply AI tools under MO2, they do so with disclosure habits, checking routines, and a clear sense of where their own professional responsibility begins.
These videos were chosen from YouTube on October 3, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.