title: Designing a Dashboard for Transparency and Control of Conversational AI
summary: 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
paper_authors._id: 666fe01119b028dd91bc9dd6
paper_authors.name: Yida Chen
paper_authors._id: 666fe01119b028dd91bc9dd7
paper_authors.name: Aoyu Wu
paper_authors._id: 666fe01119b028dd91bc9dd8
paper_authors.name: Trevor DePodesta
paper_authors._id: 666fe01119b028dd91bc9dd9
paper_authors.name: Catherine Yeh
paper_authors._id: 666fe01119b028dd91bc9dda
paper_authors.name: Kenneth Li
paper_authors._id: 666fe01119b028dd91bc9ddb
paper_authors.name: Nicholas Castillo Marin
paper_authors._id: 666fe01119b028dd91bc9ddc
paper_authors.name: Oam Patel
paper_authors._id: 666fe01119b028dd91bc9ddd
paper_authors.name: Jan Riecke
paper_authors._id: 666fe01119b028dd91bc9dde
paper_authors.name: Shivam Raval
paper_authors._id: 666fe01119b028dd91bc9ddf
paper_authors.name: Olivia Seow
paper_authors._id: 666fe01119b028dd91bc9de0
paper_authors.name: Martin Wattenberg
paper_authors._id: 666fe01119b028dd91bc9de1
paper_authors.name: Fernanda Viégas
paper_title: Designing a Dashboard for Transparency and Control of Conversational AI
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: Conversational LLMs interpretability techniques user model dashboard real-time display future directions machine learning research TalkTuner