Explain Your AI Safety Values

Interview Question

Tell me about a time in your engineering career when you identified a safety, security, reliability, or ethical risk in a system you were building that was ambiguous, non-obvious, or contested by your team. How did you evaluate the potential impact, communicate the concern against competing priorities (such as velocity or product delivery), and decide the appropriate path of action?

Additionally, how do you translate high-level AI safety principles (such as epistemic humility, responsible scaling, and mitigating tail risks) into day-to-day software engineering decisions?

What a Strong Answer Demonstrates

• Grounded Precedent over Platitudes: Anchors values in concrete, past engineering choices—such as handling data privacy leaks, edge-case vulnerability disclosures, abusive usage patterns, or flawed model pipelines—rather than reciting general ethical maxims.
• Epistemic Humility: Demonstrates willingness to interrogate one's own assumptions. Shows an understanding that risk mitigation often involves uncertain probabilities, incomplete data, and legitimate trade-offs between moving quickly and exercising caution.
• Constructive Escalation and Influence: Illustrates how to build consensus through empirical evidence, reproduction scripts, metrics, or risk modeling, rather than relying on dogmatic opposition. Demonstrates high agency without being adversarial.
• Operationalizing AI Safety: Connects software engineering fundamentals (e.g., hermetic sandboxing, deterministic testing, evaluation harnesses, canary releases, audit logging) to systemic model alignment and catastrophic risk prevention.
• Clear Ethical Thresholds: Demonstrates thoughtful discernment between acceptable operational risks that can be monitored post-launch versus unrecoverable failures that demand a complete halt.