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:

1. Transparency (internal and customer-facing)
2. Documentation (datasets, models, decisions, assumptions)
3. Bias testing and mitigation (metrics, thresholds, trade-offs)
4. Governance (roles, approvals, gates, monitoring)
5. Independent review (validation, audit)

Provide concrete steps, artifacts you would produce, and how you would operationalize these practices during development, deployment, and monitoring.