Cases in Generative AI Risks part I

This is the first of a two-class module. This class explores 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. (Note that we were previously scheduled to have a guest speaker in this class on Generative AI and Governance, but unfortunately our guest speaker was required to work with the client on a recently scheduled workshop).

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.

Materials:

Case: Pure Oils (see page 5 for example analyses to perform in Alteryx).
Slides: will be available for download by the beginning of class in either powerpoint or pdf formats.
Data: A data update may be required for this class. To ensure your files are the most up-to-date, navigate to ACCTG521_Labs folder and run the command git pull.
Analytics Tools: Alteryx Workflows and Potentially Alteryx Batch Macros
Analytics Tools: Git and GitHub

Review and Extension:

Continuing our focus on Generative AI, we will examine the risks associated with Generative AI relating to data veracity. This is the first 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.

Preparation:

1. There is no formal preparation for this class. It extends the analysis started in the Pure Oils Case from ACCTG522 Class 7.

Class Plan:

Teams: during this class, please sit in your assessment teams.

1. We will begin class with a brief review of Alteryx tool mastery levels.
2. We wil begin analysis on what will be used to help build a repeatable workflow for identifying red-flags in the Pure Oils data.
3. The Class 16 Folder contains a single file that is representative of the class submissions from ACCTG Class 7, it will serve as the macro template workflow, which will be built in this class.
4. The focus of this class is to do as comprehensive of a job at analyzing red flags and data veracity threats in the template workflow.
5. The final output from the workflow should include a conditional statement that uses the red-flags to identify whether the file passes or fails the test for it being AI generated / not real data.
6. Next class, the goal is to work on understanding how to create, edit, and troubleshoot Alteryx Batch Macros.