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Responsible AI Ethics and Data Practice · LearnSpace
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courseraIT и технологии

Responsible AI Ethics and Data Practice

Курс от Microsoft
Средний≈ 7.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Responsible AIData EthicsData GovernanceData QualityRegulatory ComplianceCloud SecurityAI literacyGovernanceThreat ModelingArtificial IntelligenceRisk AnalysisStakeholder ManagementCybersecurityAudit Working PapersEthical Standards And ConductGovernance Risk Management and ComplianceInternal AuditingBusiness EthicsPolicy DevelopmentCompliance Management

Программа курса

9 модулей · 52 учебных материалов

01AI Use-Case Scoring: Understand the Six Principles and the Review Framework5 материалов
Skipping the AI Ethics ReviewВидеоMicrosoft's Six Responsible AI Principles: What Each Requires of an AI DeploymentЧтениеRead & Evaluate AI Use-Case Intake FormВидеоHands-on Activity: Apply the Principles to an Ethical Edge CaseDIALOGUE

Учитесь у экспертов

Microsoft

Преподаватель курса

Responsible AI Ethics and Data Practice
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Обучение на Coursera

≈ 7.1 ч

9 модулей

Язык: Английский

Часть программы вашего университета
Knowledge Check: Responsible AI Principles and Review FrameworkЗадание
02AI Use-Case Scoring: Score a Form and Document a Decision6 материалов
What Makes a Proceed, Mitigate, or Reject Decision Defensible?DIALOGUEThe Proceed/Mitigate/Reject Decision Framework: Scoring Logic and Documentation RequirementsЧтениеScoring a Use-Case Intake FormВидеоHands-on Activity: Score a Use-Case Intake Form and Record a DecisionЗаданиеKnowledge Check: Scoring Logic and Decision DocumentationЗаданиеGraded Quiz: Score AI Use-CasesЗадание
03Bias Detection: Analyze a Bias-Scan Report and Escalate Findings6 материалов
What Happens When Bias Goes Undetected in a Deployed ModelВидеоBias-Scan Reports: Metrics, Indicators and SignalsВидеоDisparate Impact and Fairness Thresholds: A Reference Guide for Governance ReviewersЧтениеOpening a P1 Ethics Incident in Microsoft : Step-by-Step Reference with Annotated ScreenshotsЧтениеHands-on Activity: Analyze a Bias-Scan Report and Open a P1 Ethics IncidentЗаданиеKnowledge Check: Bias Detection and EscalationЗадание
04Bias Mitigation: Evaluate Options and Justify a Preferred Ethical Control7 материалов
When No Mitigation Option Is Perfect: A Bias Trade-off DialogueDIALOGUEFeature Reweighting vs. Post-Processing, and When to Use EachВидеоEvaluating Bias Mitigations: An Ethical and Operational Assessment FrameworkЧтениеPresenting a Mitigation Recommendation in a Design-Review Meeting: Step-by-Step Reference with Annotated ExampleЧтениеHands-on Activity: Evaluate Three Mitigation Options and Draft a Steering Committee RecommendationЗаданиеKnowledge Check: Bias Mitigation Evaluation and RecommendationЗаданиеGraded Quiz: Detect Bias & MitigateЗадание
05Dataset Ethics: Apply the Data Ethics Checklist to Approve or Reject a Dataset5 материалов
Why Model Data is a Governance RiskВидеоInternal Data Ethics GuideВидеоCompleting a Dataset Ethics Review and Documenting the Decision: Step-by-Step ReferenceЧтениеHands-on Activity: Review a Third-Party Dataset Against the Data Ethics ChecklistЗаданиеKnowledge Check: Data Ethics Checklist and Dataset ReviewЗадание
06Dataset Ethics: Analyze Lineage Metadata for Ethical Sourcing and Consent7 материалов
What Does It Mean to Really Know Where Your Training Data Came From?DIALOGUEUnderstanding Data Lineage: What Metadata Captures and What Governance Reviewers Need to SeeЧтениеTracing and Flagging Dataset IssuesВидеоConducting a Lineage Review and Flagging Non-Compliant Sources: Step-by-Step ReferenceЧтениеHands-on Activity: Trace Dataset Lineage and Identify Non-Compliant Training Data AssetsЗаданиеKnowledge Check: Lineage Metadata and Ethical SourcingЗаданиеGraded Quiz: Vet Datasets EthicallyЗадание
07Classify & Monitor Data: Apply Classification Schema and Enforce Retention Tags5 материалов
Consequences of Improper Embedding ClassificationВидеоFour-Tier Data and EmbeddingsВидеоApplying Classification Labels and Retention Tags in the Embedding Index Portal: Step-by-Step ReferenceЧтениеHands-on Activity: Classify Embedding Indexes and Enforce Retention TagsЗаданиеKnowledge Check: Data Classification and Retention TagsЗадание
08Classify & Monitor Data: Analyze Quality Dashboards and Trigger Remediation Workflows7 материалов
What Does a Data Quality Problem Actually Cost an AI System?DIALOGUEData Quality: Drift, Outliers, and ThresholdsВидеоData-Steward Remediation Workflows: Triggers, Ticket Requirements, and Escalation PathsЧтениеDrift Dashboard and Remediation TicketsВидеоHands-on Activity: Review a Drift Dashboard and Open Remediation TicketsЗаданиеKnowledge Check: Data Quality Monitoring and Remediation WorkflowsЗаданиеGraded Quiz: Classify & Monitor DataЗадание
09Project Module: Ethical AI Review Package4 материалов
Your Ethical AI Deployment NarrativeВидеоEthical AI Review Package: Requirements for the Patient Triage ModelЧтениеEthical AI Review Package: Step-by-Step Project Instructions for the Patient Triage ModelЧтениеEthical AI Review PackageЗадание