Vignettes from an AI-enhanced district town meeting
I am a fan of the national town meeting, a term introduced by Ross Perot in his 1992 campaign for President. In my view, the national town meeting must comprise many meetings at lower levels of government, including, town meetings for congressional districts. While the national town meeting will be an excellent forum for our understanding of ourselves as a nation and our preferences for the future, consensus for the practical steps and changes to the law may more commonly occur locally in congressional districts. These district meetings should be easily accessible, ubiquitously available (24x7), and operate under special rules for those relatively rare times when the representative is able to attend.
A national town meeting will be the heart of our nation’s ability to conduct itself intelligently. By providing a formal mechanism for continuous public engagement, our government will literally increase its intelligence, in the sense of being better able to understand our environment, form plans and achieve long-term goals. The United States is already in the process of becoming an intelligent being: development of collective intelligence is occurring naturally and inevitably. Constructing our national town meeting will be another step down that gradient.
In our coming world of intelligent assemblies, a congressional district (typically ~700,000 people) should itself act intelligently with respect to its self-interests. The people of the district will need to work together and in coordination with their representative’s office to sense the district’s physical, economic, and intellectual environment; plan its actions; and track its progress toward goals. Much interpretation and disagreement will be involved, and even the most gerrymandered of districts contains enough diversity of opinions to support robust debate.
Let’s see how the district town meeting works from the perspective of a small business owner, a citizen with concerns, and a lobbyist trying to pass an upcoming bill…
A small business owner asks her computer for economic trends that impact her business. The result is a map and synopsis supported by the district town meeting. The map includes detailed economic projections for the next year in the area surrounding her business, views for expected future impacts of existing state & federal laws, and some relevant bills that are under consideration. One potential bill is indicated to have significant impact. The bill is tied to a projected economic slow-down in her area over the next ten years. She asks for more information. An AI tied to the bill shows tax savings for her and her business and offers to check the impact on her customers anonymously. She checks the impact on her customer list (She does this via her personal government agent. The government does not retain her customer data.). She sees that she and her high-end customers will enjoy a net tax savings but most of her customers will suffer a net income loss. From the computed impact on her income, her customers, and her area, the computer provides a best guess of a net loss for her business with an indicated 60% certainty. The business owner registers a “no” vote on the upcoming bill but does not otherwise participate in the debate.
A concerned citizen has aging parents who require expensive medication and one of his children has a pre-existing condition. He comes to the district meeting, which is a Congressional component of the President’s desk. At the top level there’s an interface for upcoming votes, and within that module there’s an interface for the upcoming health care bill. The icon for the health care bill is glowing brightly because there’s a lot of discussion occurring, and he is tagged in much of that discussion. He sees in a glance that the representative has been personally involved in the discussion, but is not here right now. He clicks on the health care discussion icon and can see that in the eight hours since he last participated, no conclusions have been reached, there are many open questions and there are two nationally-rated postings. These summaries are curated by a mix of AI, staff, and volunteers. He looks at responses under the nationally rated posting of interest, which began with video of him confronting the representative several days ago about his daughter’s pre-existing condition. The AI organizes the responses, and he sees that there is a cluster of responses in which people have said that maybe his child should die; maybe it’s God’s will. He composes himself and tapes a video response to those people. The AI helps him edit the response for presentation and then post it. His response to the people who think God wants him to lose his health care coverage goes viral nationally over the next 36 hours.
A lobbyist is working to pass a major health care bill. Automated polling and response inferences strongly suggest that the viral video of the calm but clearly upset father may be moving support away from the bill. The lobbyist calls a friend in the representative’s office and asks to speak at the representative’s district meeting in a head-to-head, give-and-take format. She calculates that the district meeting will choose the upset father as her debate opponent, who she expects she can handily defeat in a back-and-forth debate. When the lobbyist’s presentation is announced for the following day with an open slot for the opposition, the AI schedules a vote over the next two hours to select an opposing speaker for the open debate slot. Opted-in participants throughout the district are alerted that a vote is open. The meeting vote selects the upset father. However, the father knows his limitations and asks that the district’s 3rd choice, the head cardiologist of the local hospital, speak instead. When the debate does occur, many people tune in to hear the lobbyist present her case for passage of the bill and the response from the cardiologist. Many more listen to the highlights, which are carried by news outlets as well, and a large portion of the district’s voter’s register their non-binding votes on the bill for the representative’s edification.
Somewhat later, when the Senate is preparing to vote on its version of the health care bill, a Senator reviews the straw vote outcomes of his statewide town meeting and all the state’s representative’s town meeting. The AI indicates that the most persuasive argument was the grief of parents over the anticipated loss of their children. Computed persuasion indexes show that parental grief was compelling for people in nearly all psychographic and ideological categories. The Senator requests a quiet experiment with negative ads showing lively children who die needlessly and wrecked parents saying, “You did this, Senator”. Over the next several days the computer finds that those ads will have high impact and it is unable to identify effective counter arguments. The AI provides an estimate of 2571 preventable deaths of children under 10 in his state prior to the next election if the health care law passes. The Senator is certain his opponents will not hesitate to blame him for those deaths if he votes for the health care bill.
It was not Churchill, but Abba Eban who said, “Men and Nations do behave widely once they have exhausted all other alternatives.” The national town meeting will not alter the nature of politics, but it will allow us to simulate many more alternatives as we feel our way forward. The district meetings of Congressional representatives are a critical system component.