Data Stewardship for AI: Privacy & Quality

Build Trustworthy AI—Manage Data with Precision, Privacy, and Purpose.

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

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:

To ensure AI systems are trained and tested on reliable, secure, and fair data
To bridge the gap between data science and data governance
To empower organizations to deploy ethical, compliant AI at scale
To reduce risks from poor data management in AI projects

What you will learn?

Week 1: Foundations of Data Stewardship in AI

Module 1: Principles and Roles in AI Data Stewardship

Chapter 1.1: What is Data Stewardship and Why It Matters for AI
Chapter 1.2: Responsibilities of Data Stewards in AI Projects
Chapter 1.3: Data as a Strategic Asset – Ethics and Governance
Chapter 1.4: Overview of AI Data Workflows: Collection, Curation, Use

Module 2: Privacy-Centric Data Design

Chapter 2.1: Understanding Data Privacy in the Context of AI
Chapter 2.2: Legal Frameworks: GDPR, CCPA, HIPAA, and Global Laws
Chapter 2.3: Personally Identifiable Information (PII) and Sensitive Data
Chapter 2.4: Consent, Anonymization, and Data Minimization

Week 2: Managing AI Data Quality and Lineage

Module 3: Data Quality Dimensions and Standards

Chapter 3.1: Defining Quality in AI Datasets (Accuracy, Completeness, Consistency)
Chapter 3.2: Common Sources of Bias and Error
Chapter 3.3: Tools for Validating and Profiling AI Data
Chapter 3.4: Labeling Guidelines and Quality Control in Annotation Workflows

Module 4: Data Lineage and Metadata Management

Chapter 4.1: Why Data Lineage Matters in AI
Chapter 4.2: Documenting Data Flow from Source to Model
Chapter 4.3: Metadata Standards (DCAT, Schema.org, ISO 11179)
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

Chapter 5.1: Building Governance Frameworks for AI Data
Chapter 5.2: Conducting Data Risk Assessments
Chapter 5.3: Auditing AI Data Pipelines for Compliance
Chapter 5.4: Cross-Functional Collaboration with Legal, Security, and Data Teams

Module 6: Future-Proof Stewardship and Capstone

Chapter 6.1: Designing Scalable Stewardship Processes
Chapter 6.2: Monitoring for Drift, Privacy Breaches, and Integrity Loss
Chapter 6.3: Responsible Data Offboarding and Retention Strategies
Chapter 6.4: Capstone Project – Design a Stewardship Plan for a Real AI Use Case

Intended For :

Data analysts, data engineers, AI/ML developers
Compliance officers, policy professionals, and data privacy consultants
AI product managers and quality assurance teams
Recommended: Familiarity with basic data and AI concepts