Ethical Frameworks & AI Governance

1. Obtain a synthetic dataset of resumes and outcomes, ensuring it mirrors real-world demographic imbalances.,2. Use a fairness toolkit (e.g., IBM AIF360) to quantify disparate impact ratios across gender groups.,3. Document the findings in a one-page report, outlining the likely source of bias (e.g., historical data, feature selection).,4. Propose one concrete mitigation strategy (e.g., reweighting training data, using a fairness constraint).

1. Convene a cross-functional working group (clinical, data science, legal, ethics).,2. Conduct a structured risk mapping session using a framework like the IEEE Ethically Aligned Design to identify failure modes and harms (e.g., false negatives for certain demographics).,3. Draft a preliminary model card and data sheet detailing the tool's intended use, limitations, and performance metrics across subgroups.,4. Present the assessment to leadership with a clear recommendation: proceed with specific controls, pilot with monitoring, or pause development.

1. Benchmark the firm's existing data and model governance practices against key regulatory requirements (EU AI Act's risk tiers, NYC Local Law 144).,2. Draft a charter that defines an AI Risk Management Framework, establishes an oversight board with specific authority, and mandates Conformity Assessments for high-risk systems.,3. Integrate mandatory 'gates' into the MLOps pipeline requiring ethical sign-off at data ingestion, model validation, and pre-deployment stages.,4. Design a scalable training and certification program for all technical and product personnel involved in the AI lifecycle.