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Responsible AI Ethics and Data Practice

Microsoft via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Part of

Microsoft Enterprise AI Governance, Ethics & Security

Overview

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This course focuses on the ethical and data governance dimensions of AI deployment. You’ll apply Microsoft’s six Responsible AI principles to score new use cases, detect and escalate bias in model outputs, vet third-party datasets for ethical sourcing, and classify and monitor data assets to enforce retention and quality standards. This course bridges technical controls with ethical decision-making at the managerial level. Some familiarity with AI model concepts is helpful. By the end of this course, you will be able to assess ethical AI risks, justify mitigation decisions, evaluate data quality and lineage, and produce governance documentation suitable for responsible AI review, audit preparation, and deployment approval.

Syllabus

AI Use-Case Scoring: Understand the Six Principles and the Review Framework

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 the scoring process.

AI Use-Case Scoring: Score a Form and Document a Decision

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.

Bias Detection: Analyze a Bias-Scan Report and Escalate Findings

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 for ethical oversight of deployed models.

Bias Mitigation: Evaluate Options and Justify a Preferred Ethical Control

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.

Dataset Ethics: Apply the Data Ethics Checklist to Approve or Reject a Dataset

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 governance review.

Dataset Ethics: Analyze Lineage Metadata for Ethical Sourcing and Consent

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.

Classify & Monitor Data: Apply Classification Schema and Enforce Retention Tags

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 requirements.

Classify & Monitor Data: Analyze Quality Dashboards and Trigger Remediation Workflows

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 to act.

Project Module: Ethical AI Review Package

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 governance and risk teams when preparing an AI system for internal approval, audit readiness, or deployment sign-off. Learners are expected to demonstrate not only the completion of individual tasks, but also the ability to connect ethical AI evidence into a coherent governance decision narrative—one in which use-case risk assessment, bias mitigation controls, dataset ethics, and data governance practices work together to support a clear, defensible deployment recommendation.