Talk to the City

Product overview

Talk to the City is an open-source LLM interface for improving collective deliberation and decision-making by analyzing detailed, qualitative data. It aggregates responses and arranges similar arguments into clusters.

What makes Talk to the City unique is the combination of the following features:

  • It leverages the power of frontier LLMs to analyze large datasets

  • It is designed to summarize human opinions, as opposed to just factual data

  • It automatically prepares summaries, visualizations and reports 

  • It is developed as a non-profit initiative 

Talk to the City’s data processing pipeline starts by processing a variety of data types, then uses LLMs to extract key arguments, and finally arranges similar arguments into clusters and subclusters. Users can navigate through a map of opinions and drill down to the subclusters they find most interesting.  

Examples

Recursive Public, Agenda Setting

https://tttc.dev/recursive is a report on high-priority topics in AI deployment, using data collected by the Polis tool, and letting you filter arguments by level of consensus (in partnership with Recursive Public).

GenAI Taiwan 2023

https://tttc.dev/genai reports opinions on generative AI, and can be read either in English or Mandarin. The data came from a public consultation conducted in Taiwan by Audrey Tang and the Collective Intelligence Project.

Heal Michigan

Is a report generated using video transcripts from interviews with activists in Michigan, and providing links to the source videos. Interviews were conducted in partnership with Silent Cry. Report is coming soon!

Background

The fast pace of technological development in the AI space is both a source of concern and a source of hope for democracy. On the pessimistic side, many are worried that our existing democratic processes and institutions are too slow and inefficient to address the many crises humanity is facing. The more we lose trust in the efficacy of our democratic processes, the more we lose the broad participation and agreement on legitimacy of outcomes that make democracy functional, which in turn may further damage the levels of trust that people have in democratic institutions. 

On the more optimistic side, however, there is hope that AI technologies may help us escape the spiral of declining trust by allowing us to consult the public in much larger numbers, at a much faster pace, and in much more inclusive and transparent ways that capture diversity and nuance of opinion. Making this a reality won’t be an easy task, and building sufficient public trust in AI technologies will also take some time, but now that a growing number of AI pioneers have been popularizing this idea (e.g. here and here),  we believe that, with the appropriate AI safety precautions, it is time to accelerate the design and development of open-source prototypes and begin testing them in the wild.

Research questions

We're building this tool as a proof of concept and testing it with a variety of datasets, including data extracted from twitter, blog posts, pol.is consultations, and video interviews.

Our primary goal with these first experimental deployments has been to improve our understanding of the risks and benefits of using LLMs in the context of democratic consultations, and to identify where LLMs are helpful in creating intuitive interfaces for complex datasets. In particular, we wanted to answer the following questions: 

  • Are modern LLMs already reliable enough to prove helpful? 

  • Which interfaces best help users understand large corpora of opinion data?

  • How can we mitigate natural safety and quality concerns inherent in the use of current LLMs? 

  • How can we collaborate with and bring value to the broader researcher community?