Data Stewardship for AI: Privacy & Quality
Build Trustworthy AI—Manage Data with Precision, Privacy, and Purpose.
Early access to e-LMS included
About This Course
Data Stewardship for AI: Privacy & Quality is a multidisciplinary, compliance-aware course that prepares participants to manage the data behind AI—ethically, legally, and strategically. As AI systems increasingly influence decisions and operations across industries, the quality and governance of their training data become critical. This program focuses on creating data pipelines that are high-quality, privacy-preserving, regulation-compliant, and audit-ready—empowering professionals to build AI systems that are fair, explainable, and safe.
Aim
To train professionals in the principles and practices of responsible data stewardship for AI systems, with a focus on ensuring data privacy, quality, integrity, and governance throughout the AI development lifecycle.
Program Objectives
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To ensure AI systems are trained and tested on reliable, secure, and fair data
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To bridge the gap between data science and data governance
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To empower organizations to deploy ethical, compliant AI at scale
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To reduce risks from poor data management in AI projects
Program Structure
Week 1: Foundations of Data Stewardship in AI
Module 1: Principles and Roles in AI Data Stewardship
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Chapter 1.1: What is Data Stewardship and Why It Matters for AI
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Chapter 1.2: Responsibilities of Data Stewards in AI Projects
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Chapter 1.3: Data as a Strategic Asset – Ethics and Governance
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Chapter 1.4: Overview of AI Data Workflows: Collection, Curation, Use
Module 2: Privacy-Centric Data Design
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Chapter 2.1: Understanding Data Privacy in the Context of AI
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Chapter 2.2: Legal Frameworks: GDPR, CCPA, HIPAA, and Global Laws
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Chapter 2.3: Personally Identifiable Information (PII) and Sensitive Data
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Chapter 2.4: Consent, Anonymization, and Data Minimization
Week 2: Managing AI Data Quality and Lineage
Module 3: Data Quality Dimensions and Standards
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Chapter 3.1: Defining Quality in AI Datasets (Accuracy, Completeness, Consistency)
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Chapter 3.2: Common Sources of Bias and Error
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Chapter 3.3: Tools for Validating and Profiling AI Data
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Chapter 3.4: Labeling Guidelines and Quality Control in Annotation Workflows
Module 4: Data Lineage and Metadata Management
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Chapter 4.1: Why Data Lineage Matters in AI
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Chapter 4.2: Documenting Data Flow from Source to Model
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Chapter 4.3: Metadata Standards (DCAT, Schema.org, ISO 11179)
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Chapter 4.4: Creating and Maintaining a Data Catalog for AI Systems
Week 3: Compliance, Risk, and Long-Term Stewardship
Module 5: Governance and Risk in AI Data Lifecycle
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Chapter 5.1: Building Governance Frameworks for AI Data
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Chapter 5.2: Conducting Data Risk Assessments
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Chapter 5.3: Auditing AI Data Pipelines for Compliance
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Chapter 5.4: Cross-Functional Collaboration with Legal, Security, and Data Teams
Module 6: Future-Proof Stewardship and Capstone
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Chapter 6.1: Designing Scalable Stewardship Processes
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Chapter 6.2: Monitoring for Drift, Privacy Breaches, and Integrity Loss
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Chapter 6.3: Responsible Data Offboarding and Retention Strategies
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Chapter 6.4: Capstone Project – Design a Stewardship Plan for a Real AI Use Case
Who Should Enrol?
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Data analysts, data engineers, AI/ML developers
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Compliance officers, policy professionals, and data privacy consultants
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AI product managers and quality assurance teams
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Recommended: Familiarity with basic data and AI concepts
Program Outcomes
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Build and maintain high-integrity, privacy-compliant datasets for AI
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Develop systems to monitor data quality and minimize bias
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Understand and implement regulatory frameworks in AI workflows
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Operationalize data stewardship practices across teams and systems
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Align data management with ethical AI principles
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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