Data Equity: Ensuring Fair Representation in AI Data Sets

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.