# Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain

arXiv: https://arxiv.org/abs/2405.16311 · HTML: https://mlsanity.com/paper/2405.16311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2024.

## Built on by (most cited first, 1 of 1)

- [Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines](https://mlsanity.com/paper/2505.00875/) (2025)

## Abstract (arXiv)

Explainability and transparency of AI systems are undeniably important, leading to several research studies and tools addressing them. Existing works fall short of accounting for the diverse stakeholders of the AI supply chain who may differ in their needs and consideration of the facets of explainability and transparency. In this paper, we argue for the need to revisit the inquiries of these vital constructs in the context of LLMs. To this end, we report on a qualitative study with 71 different stakeholders, where we explore the prevalent perceptions and needs around these concepts. This study not only confirms the importance of exploring the ``who'' in XAI and transparency for LLMs, but also reflects on best practices to do so while surfacing the often forgotten stakeholders and their information needs. Our insights suggest that researchers and practitioners should simultaneously clarify the ``who'' in considerations of explainability and transparency, the ``what'' in the information needs, and ``why'' they are needed to ensure responsible design and development across the LLM supply chain.