Polis is an online tool used to gather open ended feedback from large groups of people. It is well suited to gathering organic, authentic feedback while retaining minority opinions.
Problems and Purpose
Think of the comment field at the end of a survey, or the comment section at the end of a news article. Both allow participants to write down whatever they think. Humans will need to process the massive amount of text that results. This creates a burden on both survey research teams and comment moderators, respectively. It is also impossible to discern whether a given viewpoint is representative of the majority or not.
Polis overcomes these challenges and produces meaning from open-ended responses. Participants can write what they think, and they can also agree and disagree with what others are saying one comment at a time. This process happens in real time. As soon as someone writes, others can vote. Polis runs statistical analysis on these voting patterns, also in real time. It produces opinion groups and surfaces the comments that brought each group together. It also surfaces comments that found broad consensus among participants.
Polis scales to any number of participants, even millions. It accomplishes this by combining crowd behavior and machine learning. Humans can do what they are good at - reading text and forming snap judgments, as well as comparing statements. Computers can do what they are good at - finding patterns in large data sets.
Polis wanted people to feel safe.
Polis wanted people to feel listened to.
Polis wanted people to be able to jump into a conversation at any time in its life-cycle.
Polis did not want them to feel intimidated or exhausted by the complexity of what had already occurred. That meant ditching threading and direct replies, so that comments didn't twist into a tangled nest. Polis wanted everyone to be able to get a sense of what others felt, and what the consensus was, in seconds.
Polis wanted to preserve minority opinions. Polis doesn't use downvoting to invalidate upvoting. Polis doesn't use upvoting to invalidate downvoting. If it worked like most other comment systems do: 200 upvotes + 200 downvotes = net zero votes. That math obscures the fact that there was a minority who felt differently, which we take as data to preserve. This took the form of creating and showing opinion groups.
Polis wanted to create a system that increased the number of roles available. More roles means more data, and more interesting outcomes. Somewhere south of 1% of users comment. Most users lurk; that is, they read but don't interact in the community. Pol.is typically sees 10x more people vote than those who comment.
Polis wanted to produce lots of usable data.
Polis wanted to show people as grouped with those who felt similarly. It's satisfying and validating to find your tribe.
Polis wanted to restrict the power of individuals to ruin a conversation with bad behavior.
Polis wanted to avoid the echo chamber effect of social networks.
Most generally, Polis wants to:
in all kinds of organizations of people anywhere on Earth.
Origins and Development
Pol.is was conceived around the time of Occupy Wall Street and the Arab Spring.
The objective was to create a comment system to be able to handle 'big' and stay coherent. If millions of people were going to show up to a conversation, the internet needed something that would scale up.
In Zuccotti Park, public conversation tech in OccupyWallStreet utilized classic forums with topics and replies (see Nycga.net and Occupywallst.org/forum). There was some prototype location-specific anonymous messaging, a preface to what FireChat would become in Hong Kong’s Umbrella Movement. Briefly, an anonymous txt2projection installation “Our Wall,” sought to amplify thoughts and ideas in and around the park without actually being loud.
The iconic technology of Occupy was the People’s Mic, by which the crowd turns themselves into a speaker system to have a conversation at scale: Mic Check! + twinkling fingers to indicate agreement. The modes of interaction in Occupy amplified individual voices into a cacophony, and out of that noise, the loudest discernible voices were the ones jockeying to speak on behalf of everyone else. Not a big difference from US speech-making personalities. Fewer provisions were made to pick up signals from a broader base of quieter folks, or to identify points of consensus within complex, divisive issues.
Colin Megill, one of the founders of Pol.is, has said that watching the People’s Mic in action, as well as the communication challenges faced by Arab Spring organizers in Egypt and Iran, inspired the creation of Pol.is. As he says,
"We wanted a comment system to be able to handle large populations and stay coherent, while preserving minority opinions and producing insights automatically. Artificial Inteligence made that possible. We wanted people to feel safe, listened to and be able to jump in and out as they please. Overall, we wanted to make it easier to successfully decentralize power in organizations of all kinds."
How it Works
Pol.is is a survey technology where the user clicks “agree,” “disagree,” or “pass” in response to statements others have contributed. The user can also enter their own statement for others to take positions on. Pol.is clusters users who voted similarly into opinion groups using real-time machine learning (artificial intelligence), and visualizes those groups in real time. Polis visually defines and gives space to divergent opinion groups and breaks the community’s deadlock by identifying the points of consensus.
Pol.is is a way to gather open-ended feedback from large groups of people. The polls can be anonymous or linked with social media accounts. A graphic interface shows how opinion clusters emerge, cluster, respond, divide, and recombine; this is possible because pol.is creates and analyzes a matrix comprising what each person thinks about every comment. Minority opinions are as well-defined as the majority opinions are, “dissent is data.” Check this illustrated blog post about the evolution of their user interface: https://blog.pol.is/the-evolution-of-the-pol-is-user-interface-9b7dccf54...
This technology only became possible since 2011 with the advent of near ubiquitous mobile connectivity, the real-time web, web-based data visualization, and neural networks (where the computer learns the rules itself instead of being hand-coded by software engineers; recommender engines like Netflix/Spotify and machine vision both use these kinds of algorithms).
Because anyone can enter a new statement, the agenda-setting power is held by the people, a critical advance on a very sticky sticking point for mass decision making. I think of this interface as the online counterpart of paper-tech methods of “open space technology”—you may have experienced a more popular but watered-down version called “unconference,” which maximizes the number of presenter-audience relationships, but does not attempt to support group decision-making.
In the 40-odd-year tradition of open space technology, individuals write the topics they want to address on pieces of paper, then the group works together to cluster the topics and place them into a schedule for dedicated discussion time. This analog method is in wide use today by groups self-organizing meetings, and should be given credit for being able to scale to many hundreds of people with a single ream of printer paper, some markers, and a bit of tape. Pol.is, however, is made for the masses.
Analysis and Lessons Learned
Polis went beyond its origins from Occupy Wall Street. Its main outcome today, in 2016, is its integration to vTaiwan (https://www.participedia.net/en/methods/vtaiwan), a political innovation in Taiwan that promotes the shortening of distance between representatives and represented.
Once vTaiwan deployed pol.is, participation scaled a hundredfold, the complexity of issues grappled with increased, and the volunteer moderators were no longer needed during the “crowd-sourced agenda setting” phase of the project. Polis simplified it all. After years of closely iterating with the vTaiwan team, pol.is was open sourced, greenlighting its long-term integration into governing processes.
Pol.is also has been approached by academics working with the municipal government of Rome in 2016. Multiple US agencies at the state and federal level have also demonstrated interest.
Polis aims to change the relationship between citizens and governments in all levels in all places by making feedback something that happens automatically, not something governments have to “go get.” The platform makes it so simple to deploy on a daily or weekly basis that there’s no excuse to not find out what a given population thinks. The use of Artificial Intelligence will dramatically change the calculation for robust social research.
Getting high dimensional, organic feedback from the population during a problem identification phase—as early as possible in the formation of rules—is categorically different from voting. In voting the cake is baked, and there are literally hundreds of issues at stake. The goal is to engage citizens far earlier, when everyone is arguing over the ingredients. It gives citizens much more leverage in shaping policy, and involves them at the phase the process is most accessible, and their input is most valuable as well.
As the complexity of the economy increases, it’s critical to increase the speed with which governments are able to respond to regulatory demands in a collaborative, transparent, and sophisticated way. Polis can help governments to move faster and with more confidence to meet complex challenges posed by new technologies, while embracing diversity of thought and balancing interest groups.
"Mission and Values," Pol.is, July 17, 2014 https://pol.is/company
Lead image: Pol.is https://goo.gl/21c9jJ