Understanding Stakeholders' Perceptions and Needs Across the LLM Supply Chain

TL;DR

This paper investigates diverse stakeholders' perceptions and needs regarding explainability and transparency in large language models through a qualitative study, emphasizing the importance of stakeholder-specific considerations for responsible AI development.

Contribution

It highlights the necessity of identifying and addressing the varied stakeholder needs in LLM transparency and explainability, providing insights for more responsible AI practices.

Findings

Stakeholders have diverse perceptions of explainability in LLMs.

Identified often overlooked stakeholders and their specific information needs.

Emphasized the importance of clarifying 'who', 'what', and 'why' in transparency efforts.

Abstract

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.