早期开发者如何理解多智能体LLM系统的透明性
"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency
- 通过访谈13名开发者，提炼出透明性的多维度认知框架
- 发现透明性涵盖可复现、调试、边界设定、可视化与审计五方面
- 适合关注AI可解释性与人机协同设计的研究者与工程师
多智能体大语言模型系统正迅速发展，但其分布式架构中的透明性——负责任AI的核心——仍缺乏明确定义，尤其在智能体间协调与编排的复杂背景下。本文开展了一项针对多智能体LLM系统早期采用者的实证研究，这些使用者同时也是系统构建者。我们对来自[大型科技组织]的13位早期采用者进行了半结构化访谈，并运用主题分析识别出重复出现的认知模式。参与者提出了多样但互补的透明性框架，包括可复现性、可调试性、边界设定、可视化和审计。这些视角覆盖了透明性内涵、重要性及实现方式等问题。我们将其整合为一个多维框架，强调透明性是面向开发者、用户与治理的具身化社会技术实践，为未来人机交互与人工智能设计研究提供指引，以对齐预期与能力。
原文摘要 · Abstract (English)
Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these distributed architectures, which have complexities of inter-agent coordination and orchestration. In this paper, we present one of the first empirical study of how early adopters of multi-agent LLM systems, who are both the builders and users, understand and practice transparency. We conducted semi-structured interviews with 13 early adopters in [Large Technology Organization] and applied thematic analysis to identify recurring patterns. Participants articulated divergent yet complementary framings of transparency, including reproducibility, debugging, boundary-setting, visualization, and auditing. These perspectives spanned questions of what transparency entails, why it matters, and how it is achieved. We synthesize these into a multidimensional framework, which is developer, user, and governance-focused positioning transparency as a situated socio-technical practice that informs future HCI and AI design and research around aligning expectations and capacities of their intended audiences.
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