Conversational LLMs function as black box systems, leaving users guessing
about why they see the output they do. This lack of transparency is potentially
problematic, especially given concerns around bias and truthfulness. To address
this issue, we present an end-to-end prototype-connecting interpretability
techniques with user experience design-that seeks to make chatbots more
transparent. We begin by showing evidence that a prominent open-source LLM has
a "user model": examining the internal state of the system, we can extract data
related to a user's age, gender, educational level, and socioeconomic status.
Next, we describe the design of a dashboard that accompanies the chatbot
interface, displaying this user model in real time. The dashboard can also be
used to control the user model and the system's behavior. Finally, we discuss a
study in which users conversed with the instrumented system. Our results
suggest that users appreciate seeing internal states, which helped them expose
biased behavior and increased their sense of control. Participants also made
valuable suggestions that point to future directions for both design and
machine learning research. The project page and video demo of our TalkTuner
system are available at this https URL

通过结合解释性技术和用户体验设计，我们提出了一个端到端原型，旨在提高聊天机器人的透明度，通过检测系统的内部状态，展示与用户的年龄、性别、教育水平和社会经济地位相关的数据，设计了一个实时显示此用户模型的仪表板，并利用该仪表板控制用户模型和系统行为，研究结果表明用户喜欢看到内部状态，其有助于揭示偏见行为并增强他们的控制感，参与者还提出了有价值的建议，指出未来的设计和机器学习研究方向。

设计对话式人工智能透明度和控制的仪表盘

Designing a Dashboard for Transparency and Control of Conversational AI

Among creative professionals, Generative Artificial Intelligence (GenAI) has
sparked excitement over its capabilities and fear over unanticipated
consequences. How does GenAI impact User Experience Design (UXD) practice, and
are fears warranted? We interviewed 20 UX Designers, with diverse experience
and across companies (startups to large enterprises). We probed them to
characterize their practices, and sample their attitudes, concerns, and
expectations. We found that experienced designers are confident in their
originality, creativity, and empathic skills, and find GenAI's role as
assistive. They emphasized the unique human factors of "enjoyment" and
"agency", where humans remain the arbiters of "AI alignment". However, skill
degradation, job replacement, and creativity exhaustion can adversely impact
junior designers. We discuss implications for human-GenAI collaboration,
specifically copyright and ownership, human creativity and agency, and AI
literacy and access. Through the lens of responsible and participatory AI, we
contribute a deeper understanding of GenAI fears and opportunities for UXD.

研究发现，经验丰富的设计师对于 Generative Artificial Intelligence（GenAI）的作用持支持态度，强调了人类在享受和作为 “AI 合作伙伴” 的裁决者方面的独特优势，然而，技能退步、工作替代和创造力衰竭可能会对初级设计师产生不利影响。研究探讨了人类与 GenAI 的合作的责任和参与度，包括版权和所有权，人类的创造力和主权，以及 AI 读写能力和使用方面的机会。

用户体验设计专业人员对生成人工智能的感知

User Experience Design Professionals' Perceptions of Generative  Artificial Intelligence

In recent years, the field of explainable AI (XAI) has produced a vast
collection of algorithms, providing a useful toolbox for researchers and
practitioners to build XAI applications. With the rich application
opportunities, explainability is believed to have moved beyond a demand by data
scientists or researchers to comprehend the models they develop, to an
essential requirement for people to trust and adopt AI deployed in numerous
domains. However, explainability is an inherently human-centric property and
the field is starting to embrace human-centered approaches. Human-computer
interaction (HCI) research and user experience (UX) design in this area are
becoming increasingly important. In this chapter, we begin with a high-level
overview of the technical landscape of XAI algorithms, then selectively survey
our own and other recent HCI works that take human-centered approaches to
design, evaluate, and provide conceptual and methodological tools for XAI. We
ask the question "what are human-centered approaches doing for XAI" and
highlight three roles that they play in shaping XAI technologies by helping
navigate, assess and expand the XAI toolbox: to drive technical choices by
users' explainability needs, to uncover pitfalls of existing XAI methods and
inform new methods, and to provide conceptual frameworks for human-compatible
XAI.

本文介绍了可解释 AI 的技术领域以及人们对人机交互和用户体验设计日益重视，在 XAI 的设计、评估、概念和方法论工具方面提供人本方法的作用。本文旨在提供人本的 XAI 设计，并帮助传达和扩展已有的 XAI 工具箱。