Data Equity: Ensuring Fair Representation in AI Data Sets
1h 25mBeginner2025-07-16
Authors
Mary-Frances Winters
Founder and CEO of The Winters Group, Inc.
Mareisha Reese
Course details
This course digs deep into the benefits of AI and the dire consequences if the data used to create AI tools does not consider fair representation. Instructors Mareisha Reese and Mary-Frances Winters share some of the key opportunities and challenges in ensuring fairness in AI datasets. They cover AI definitions related to data analysis, collection, generation, challenges in sourcing diverse datasets, bias, legal and ethical foundations, real-world applications in HR, marketing, and more.
Learning objectives
Define fair representation and why it is so important.
Outline the challenges in sourcing diverse data in dataset creations.
Delineate ways to embed fairness into the AI lifecycle.
Discuss GenAI and its role in data fairness.
Share examples in HR, marketing, health care, and financial services.
Identify legal and ethical implications.
Outline a future where fairness in AI is standard practice.
Learning objectives
Define fair representation and why it is so important.
Outline the challenges in sourcing diverse data in dataset creations.
Delineate ways to embed fairness into the AI lifecycle.
Discuss GenAI and its role in data fairness.
Share examples in HR, marketing, health care, and financial services.
Identify legal and ethical implications.
Outline a future where fairness in AI is standard practice.
Skills covered
Data GovernanceResponsible AIData PrivacyGenerative AIData AnalysisArtificial Intelligence (AI)Data ScienceBusiness Analysis and StrategyBusiness Software and ToolsDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - What is fair representation, and why is it important
1. Breaking Down AI into Its Parts
- 02 - What is AI, and what are its components
- 03 - AI - The big picture
- 04 - Machine learning and fairness
- 05 - Deep learning and fairness
- 06 - Generative AI and fairness
2. The AI Life Cycle
- 07 - The AI life cycle and the risk of inequity
- 08 - Data collection to ensure fairness
- 09 - Data preparation for fairness
- 10 - Model development and evaluation for equity
- 11 - Deployment and monitoring of the AI model
3. Examples of AI Applications
- 12 - Equitable AI in HR
- 13 - Equitable AI in marketing
- 14 - Equitable AI in healthcare
- 15 - Equitable AI in financial services
4. Legal and Ethical Considerations
- 16 - Legal issues for fair AI data representation
- 17 - Ethical issues for fair AI data representation
5. The Future of AI
- 18 - Job transformation with AI
- 19 - AI in everyday life
- 20 - AI's role in enhancing human capabilities
Conclusion
- 21 - Next steps for fair representation in AI datasets