Responsible AI Interview Questions
Turning responsible AI into requirements a team can test, regulation such as the EU AI Act, and being honest with users about what the AI does. Asked across AI roles, and often in their final rounds. 62 questions across 3 subtopics, each with a model answer.
Responsible AI Requirements
How to turn responsible AI principles into requirements a team can test, which safety requirements every AI feature needs, and what to do when a feature works for most users but fails one group.
- How do you turn a responsible AI principle into a testable product requirement?
- What safety requirements belong in every AI PRD regardless of feature?
- Describe how you would assess a feature for potential harm before building it.
- Explain the difference between a safety issue and a quality issue.
- How would you handle a feature that works well overall but poorly for one demographic?
- What is a content policy and who should own it in a product organization?
- Design the guardrails for an AI feature aimed at teenagers.
- How do you balance a refusal rate that is too high against one that is too low?
AI Compliance and the EU AI Act
What regulation such as the EU AI Act means for a product, how risk categories work in practice, and how to bring legal into an AI project early without slowing it down.
- What does the EU AI Act require of a product like the one you last worked on?
- Explain risk categorization under the EU AI Act in product terms.
- How do you bring legal into an AI project early without slowing it down?
- What questions will your legal team ask about training data, and how do you prepare?
- Describe the copyright exposure of a generative feature.
- How do you handle a customer contract that prohibits any use of their data for model improvement?
- What disclosure obligations apply when users interact with an AI system?
- Explain data residency requirements and how they constrain model choice.
AI Trust, Transparency and Explainability
What users need to see before they trust an AI recommendation, how to label AI output honestly, and how to design opt outs that respect users without breaking the product.
- What does a user need to see to trust an AI recommendation?
- Explain the difference between explainability and transparency in a product context.
- How do citations change user behaviour, and what happens when they are wrong?
- Design the disclosure that tells a user they are talking to an AI.
- When does showing the model's reasoning help, and when does it reduce trust?
- Critique a design that surfaces a chain of thought to end users.
- How much should you tell users about which model powers a feature?
- Describe how you would design for a user who wants to audit an AI decision.