Data Veracity: Generative AI 2

Why is this important?

As generative AI increasingly enables the rapid creation of highly realistic datasets, the risks of fabricated operational data continue to grow. Understanding and detecting synthetic data manipulation is essential for future business leaders, auditors, and data analysts, who must protect organizations and stakeholders from the financial and reputational harm of data misrepresentation. This class provides students with foundational skills in data integrity assessment and visual analytics, preparing them to navigate the emerging challenges generative AI introduces in verifying workforce and operational data. Through real-world scenarios and practical tools, students learn to uphold transparency and accuracy in business reporting.

What will we do?

This is the second of a two-class module. This class continues to explore the implications of generative AI in creating fraudulent operational data, focusing specifically on HR records and workforce metrics. Students will examine a synthetic dataset designed to misrepresent workforce size and productivity, gaining hands-on experience in identifying red flags and anomalies. Through data analysis and an interactive dashboard, they’ll detect signs of data fabrication, such as artificial employee ID patterns, unrealistic salary distributions, and implausible position histories. The session culminates in a group discussion on the ethical and strategic implications of AI-manipulated data in business contexts, empowering students with critical data forensics and analytic visualization skills.

How this relates to other classes:

Continuing our focus on Generative AI, we will examine the risks associated with Generative AI relating to data veracity. This is the second of a two-class module which will explore how to identify data veracity red-flags, and visualize that risk using dashboards. This class is related to data created as part of the Pure Oils case covered earlier.