Designing a Dashboard for Transparency and Control of Conversational AI
Authors: name: Yida Chen, name: Aoyu Wu, name: Trevor DePodesta, name: Catherine Yeh, name: Kenneth Li, name: Nicholas Castillo Marin, name: Oam Patel, name: Jan Riecke, name: Shivam Raval, name: Olivia Seow, name: Martin Wattenberg, name: Fernanda Viégas

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 https://bit.ly/talktuner-project-page