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Responsible AI in Practice: Fairness, Bias & Explainability · LearnSpace
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Responsible AI in Practice: Fairness, Bias & Explainability

Курс от Edureka
Начальный≈ 7.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course introduces the foundations and practical implementation of Responsible AI, focusing on building AI systems that are fair, transparent, interpretable, and privacy-aware. You’ll begin by exploring fairness metrics, bias mitigation strategies, and explainability techniques such as LIME, SHAP, and counterfactual explanations. The course then covers privacy risks, differential privacy, and the trade-offs between fairness, privacy, and model accuracy in real-world AI systems. By the end of this course, you will be able to: - Explain fairness, interpretability, and privacy concepts in AI - Analyze AI models using explainability and fairness techniques - Apply bias mitigation and privacy-preserving methods - Evaluate trade-offs in responsible AI system design Designed for AI practitioners, analysts, and technology professionals, this course provides a practical approach to building responsible and trustworthy AI systems. To be successful, learners should have a basic understanding of AI and machine learning concepts. Start your journey into Responsible AI and learn how to design AI systems that are fair, transparent, and trustworthy.

Навыки, которые вы освоите

Responsible AISecurity StrategyRisk AnalysisArtificial Intelligence and Machine Learning (AI/ML)GovernanceDecision IntelligenceData EthicsInformation PrivacyMachine LearningMachine Learning MethodsStakeholder AnalysisEthical Standards And ConductRisk ManagementRisk MitigationAI SecurityModel EvaluationBusiness Risk ManagementTrustworthinessAI literacySecurity Management

Программа курса

4 модулей · 54 учебных материалов

01Bias Measurement and Mitigation17 материалов

Implementing Fairness Metrics

Course Introduction: Responsible AI in Practice: Fairness, Bias & ExplainabilityВидеоCourse Syllabus: Responsible AI in Practice: Fairness, Bias & ExplainabilityЧтениеFrom Definitions to Metrics: Applying Fairness MetricsВидеоHands-On: Comparing Fairness Metrics on a Hiring ModelВидео

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Edureka

Преподаватель курса

Responsible AI in Practice: Fairness, Bias & Explainability
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Обучение на Coursera

≈ 7.8 ч

4 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Испанский, Японский

Часть программы вашего университета
Hands-On: Interpreting Fairness Metrics Across GroupsВидео
Label Bias and Proxy Ground Truth RisksВидео
Hands-On: Counterfactual Fairness Testing with Causal GraphsВидео
Fairness Metrics Implementation GuideЧтение
Knowledge Check: Implementing Fairness MetricsЗадание

Bias Mitigation and Trade-Offs

Bias Mitigation StrategiesВидеоHands-On: Comparing Mitigation Strategies on the Hiring ModelВидеоFairness–Accuracy Trade-OffsВидеоSynthetic Data for Fairness: Methods & RisksЧтениеBias Mitigation and Trade-OffsЗадание

Module Wrap-Up and Assessment

Exploring Fairness, Bias, and Responsible AI PracticesDIALOGUEModule Summary: Bias Measurement and MitigationЧтениеKnowledge Check: Bias Measurement and MitigationЗадание
02Advanced Model Interpretability15 материалов

Local and Global Interpretability Methods

Model Interpretability: Foundations and ApproachesВидеоExplaining Model Predictions using LIME and SHAPВидеоHands-On: Debugging a Loan Model with SHAPВидеоCounterfactual Explanations: Generation, Plausibility, and SparsityВидеоComparing and Understanding XAI MethodsЧтениеLocal and Global Interpretability MethodsЗадание

Explanation Quality and Evaluation

Evaluating Explanation Fidelity in Interpretable AI SystemsВидеоStability and Robustness in AI ExplanationsВидеоHands-On: Detecting Unfaithful or Misleading ExplanationsВидеоLimits of Post-Hoc InterpretabilityВидеоEvaluating Explanation Quality: Metrics and MethodsЧтениеExplanation Quality and EvaluationЗадание

Module Wrap-Up and Assessment

Understanding Explainability and Trust in AI ModelsDIALOGUEModule Summary: Advanced Model InterpretabilityЧтениеKnowledge Check: Local and Global Interpretability MethodsЗадание
03Privacy Attacks, Defenses, and Trade-Off's17 материалов

Technical Privacy Attacks and Defenses

Membership Inference AttacksВидеоHands-On: Running a Membership Inference Attack on a Trained ModelВидеоModel Inversion and Attribute Inference AttacksВидеоUnderstanding Differential Privacy MechanismsВидеоHands-On: Comparing Private vs. Non-Private Model PerformanceВидеоHands-On: Evaluating Privacy Leakage and Model Trade-offsВидеоPrivacy Attacks and Differential Privacy: Technical HandbookЧтениеTechnical Privacy Attacks and DefensesЗадание

Multi-Objective Trade-Offs

The Impossibility Triangle: Fairness, Privacy, and AccuracyВидеоHands-On: Interactive Pareto Frontier ExplorerВидеоValue-Sensitive DesignВидеоHands-On: Building a Trade-Off Decision Record for Stakeholder ReviewВидеоMulti-Objective Optimization for Responsible AIЧтениеMulti-Objective Trade-OffsЗадание

Module Wrap-Up and Assessment

Balancing Privacy, Security, and Responsible AI Trade-OffsDIALOGUEModule Summary: Privacy Attacks, Defenses, and Trade-Off'sЧтениеKnowledge Check: Privacy Attacks, Defenses, and Trade-Off'sЗадание
04Course Wrap-Up and Assessments5 материалов

Course Conclusion

Practice Project: Responsible AI Evaluation and Trade-Off AnalysisЧтениеEnterprise Responsible AI Simulation: Fairness, Explainability, Privacy, and Trustworthy AI Decision-MakingDIALOGUEEnd Course Knowledge Check: Responsible AI in Practice: Bias, Explainability & PrivacyЗаданиеResponsible AI in Practice: Bias, Explainability & PrivacyЗаданиеCourse Summary: Responsible AI in Practice: Fairness, Bias & ExplainabilityВидео