Designing a Dashboard for Transparency and Control of Conversational AI
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
666fe01119b028dd91bc9dd6
Yida Chen
666fe01119b028dd91bc9dd7
Aoyu Wu
666fe01119b028dd91bc9dd8
Trevor DePodesta
666fe01119b028dd91bc9dd9
Catherine Yeh
666fe01119b028dd91bc9dda
Kenneth Li
666fe01119b028dd91bc9ddb
Nicholas Castillo Marin
666fe01119b028dd91bc9ddc
Oam Patel
666fe01119b028dd91bc9ddd
Jan Riecke
666fe01119b028dd91bc9dde
Shivam Raval
666fe01119b028dd91bc9ddf
Olivia Seow
666fe01119b028dd91bc9de0
Martin Wattenberg
666fe01119b028dd91bc9de1
Fernanda Viégas
Designing a Dashboard for Transparency and Control of Conversational AI
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
A prototype combining interpretability techniques and user experience design enhances transparency in conversational LLMs, allowing users to see and control the system's behavior, and exposing biased responses.
Conversational LLMs interpretability techniques user model dashboard real-time display future directions machine learning research TalkTuner