PB01
Understanding data-centric AI
This course explores the principles and practices of data-centric AI from an organisational and governance perspective.
You will examine how data quality, bias, privacy, security and lifecycle decisions directly influence AI outcomes, risk, exposure and accountability, particularly in regulated and high assurance environments.
As AI adoption accelerates, standards play a critical role in translating best practice and ethical intent into practical, repeatable controls.
Throughout this course, you will be guided towards the standards and frameworks that are most relevant for supporting robust data governance and responsible AI use, helping you make informed decisions in complex and evolving AI landscapes.
Learning outcomes
After completing this course, you will be able to:
- Define the concept of data-centric AI and explain its benefits.
- Describe the crucial role of data in AI system performance.
- Identify and explain key data challenges in managing AI, and the primary data risks for AI systems.
- Identify the primary data risks for AI systems.
- Assess the quality of data inputs into an AI system.
- Apply a data-centric AI methodology to a fictitious organisational scenario, addressing specific data acquisition challenges.
- Outline and differentiate various approaches for implementing a data-centric data management in AI development.
Topics
- What is data-centric AI?
- Diligence in data acquisition for AI systems
- Employ effective data management
- Data privacy and security risks
- Data governance
- Mitigate data bias and discrimination