Responsible AI Ethics and Data Practice

Overview

Syllabus 9

1. AI Use-Case Scoring: Understand the Six Principles and the Review Framework 35 minutes

   This module introduces Microsoft's six Responsible AI principles and the use-case intake review framework, covering what each principle requires of an AI deployment, how principles are applied to score a specific use case, and what a well-structured intake form looks like before a reviewer begins t…

2. AI Use-Case Scoring: Score a Form and Document a Decision 1 hour

   This module puts the review framework into practice. Learners score a complete use-case intake form against all six Responsible AI principles, assign an overall proceed, mitigate, or reject decision, and document the required follow-up actions for the product team.

3. Bias Detection: Analyze a Bias-Scan Report and Escalate Findings 40 minutes

   This module develops learners' ability to interpret a pre-run bias-scan report, identify statistically significant bias indicators such as disparate impact across demographic attributes, and escalate findings as a P1 ethics incident — the foundational skill for any AI governance lead responsible fo…

4. Bias Mitigation: Evaluate Options and Justify a Preferred Ethical Control 55 minutes

   This module develops learners' ability to evaluate multiple bias mitigation strategies — including Feature Reweighting and post-processing approaches — assess each against ethical and operational criteria, and justify a preferred control in a format suitable for steering-committee review.

5. Dataset Ethics: Apply the Data Ethics Checklist to Approve or Reject a Dataset 35 minutes

   This module develops learners' ability to evaluate a third-party dataset against an Internal Data Ethics Checklist—assessing sourcing transparency, consent documentation, representational fairness, and licensing compliance—and to produce a documented approve or reject decision that can withstand go…

6. Dataset Ethics: Analyze Lineage Metadata for Ethical Sourcing and Consent 1 hour

   This module develops learners' ability to trace dataset provenance using lineage metadata—identifying the origin, transformation history, and consent status of each training data source—and flag assets that lack the documentation required for ethical use in model training.

7. Classify & Monitor Data: Apply Classification Schema and Enforce Retention Tags 40 minutes

   This module develops learners' ability to evaluate vector-store embeddings against a four-tier data classification schema, assign the appropriate sensitivity label, and enforce the corresponding retention tag — producing a classified and tagged embedding index that meets policy and audit requiremen…

8. Classify & Monitor Data: Analyze Quality Dashboards and Trigger Remediation Workflows 1 hour

   This module develops learners' ability to interpret data-quality dashboard metrics — including feature drift and outlier detection — assess whether quality thresholds have been breached, and trigger data-steward remediation workflows with documented tickets that give stewards everything they need t…

9. Project Module: Ethical AI Review Package 35 minutes

   In this project, learners produce a portfolio-ready Ethical AI Review Package that consolidates all Responsible AI work completed throughout LC 2 into a single integrated governance artifact. The resulting deliverable reflects the type of end-to-end Responsible AI review documentation used by AI go…

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Taught by

• Microsoft