Ensure model ethics and governance
Overview
This question evaluates a Data Scientist's competence in model ethics and governance, including transparency, documentation, bias testing and mitigation, governance roles and approvals, and independent review applied across the ML lifecycle.
Citibank
Jul 26, 2025, 12:00 AM
Data Scientist
Technical Screen
Behavioral & Leadership
0
0
Ensuring Model Ethics Across Development and Deployment
Context
You are interviewing for a Data Scientist role in a highly regulated financial institution. Given past industry issues (e.g., high‑pressure sales practices and customer harm), describe how you would ensure model ethics end‑to‑end.
Prompt
Outline a practical, end-to-end approach for ensuring model ethics across the ML lifecycle. Specifically address:
- Transparency (internal and customer-facing)
- Documentation (datasets, models, decisions, assumptions)
- Bias testing and mitigation (metrics, thresholds, trade-offs)
- Governance (roles, approvals, gates, monitoring)
- Independent review (validation, audit)
Provide concrete steps, artifacts you would produce, and how you would operationalize these practices during development, deployment, and monitoring.
Solution
Login required
Show