This one sprawled in a way I wasn't ready for.
Frame your answer around a lifecycle approach to ethical AI, emphasizing that ethics must be embedded from problem definition through deployment and monitoring. Highlight specific practices like documentation, bias testing, and governance, and connect them to preventing past sales practice scandals. Show awareness of regulatory and reputational risks in banking.
Pro tip: Reference Citibank's own ethical AI principles or industry frameworks like the EU AI Act to show you've done your homework and can align with company values. Also, emphasize that ethical AI is not just compliance but a competitive advantage in building customer trust.
Collaborate with legal, compliance, and business stakeholders to define ethical boundaries and success metrics beyond accuracy, such as fairness and transparency. Document these requirements in a model charter.
Maintain detailed model cards and datasheets for datasets, documenting intended use, limitations, and performance across subgroups. Ensure transparency by sharing appropriate documentation with stakeholders and regulators.
Perform comprehensive bias testing using fairness metrics (e.g., disparate impact, equal opportunity) across protected groups. Validate models with independent review teams and red-teaming exercises.
Set up a cross-functional AI ethics committee with clear escalation paths. Define roles for model owners, validators, and auditors, and integrate ethical checks into the MLOps pipeline.
Continuously monitor model performance and fairness in production, with automated alerts for drift or bias. Conduct periodic independent audits and update models as needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.