| Creators: |
Hofmann, Benjamin and Engel, Tobias and Cenk, Gökhan |
| Title: |
Trustworthiness of AI Models: The Example of Supply Chain Transparency |
| Item Type: |
Conference or Workshop Item |
| Event Title: |
(Proceedings of the) 20th Midwest Association of Information Systems (MWAIS) |
| Event Location: |
Stillwater, OK, USA |
| Event Dates: |
May, 13-15, 2025 |
| Projects: |
TTZ Leipheim |
| Date: |
14 May 2025 |
| Divisions: |
Informationsmanagement |
| Abstract (ENG): |
This paper explores the challenges of creating transparency for supply chain networks including all partners, levels, and nodes using Large Language Models (LLMs). We developed a script allowing us to visualize supply chain networks with data generated by LLMs. To ensure the correctness of the results, we propose a procedure with a pre-evaluated dataset as the basis for the assessment of the generated data. Based on preliminary results, we suggest a process including a methodological evaluation approach to quantify and address the contextual accuracy of the results. The findings aim to enhance the consistency, accuracy, and trustworthiness of LLMs for using and creating transparency among upstream and downstream partners in supply chain networks. |
| Forthcoming: |
No |
| Language: |
English |
| Uncontrolled Keywords: |
Trustworthiness, Network transparency, LLM accuracy, LLM consistency |
| Citation: |
Hofmann, Benjamin and Engel, Tobias and Cenk, Gökhan
(2025)
Trustworthiness of AI Models: The Example of Supply Chain Transparency.
In: (Proceedings of the) 20th Midwest Association of Information Systems (MWAIS), May, 13-15, 2025, Stillwater, OK, USA.
|
