On the other hand you can test a neural network a million times, exhaustingly mapping its biases and failures. You can't do that with a human, you got to take a chance.

Yes you can do a million tests but the biases and failures of the neural net are chosen by human developers, a miniscule subset of citizens who themselves have some form of bias depending on their educational background, places where they grew up, family and friends etc. While you can replace a human in a powerful position with another one, since we do not know how to surgically correct individual weights in order to remove a specific decisional bias from a neural net, we can only retrain it and hope for the best. Because we are humans ourselves, we can understand the incentives of other humans and create adequate mechanisms to correct for their selfishness and their bias but what would be the incentive of an all powerful neural net? Which loss is it minimizing? And how does it know the citizens' preferences in the present and in the future? If the citizens stop feeding it (accurate) information at one point in time, will it still be the benevolent dictator that it was supposed to be?

*Edit: Another counterargument that generally applies to neural nets making decisions for human activities is the argument of accountability. While you can put on trial a bad politician for their harmful-to-society decision making, who is to blame when the neural net will inevitably spew the wrong output on an issue that it has not encountered before and a policy decision will be made based on that? Will we put the developers on trial?

Apparently GPT-3 has learned a large number of personality types and their fine grained biases, well enough to simulate a poll with good accuracy. This means you can poll it any time to check how a population of choice would have reacted had something happened. The language model does not carry the just biases of its builders, it learns all biases equally, you just need to specify the desired bias (personality type) when you call it. The responsibility falls with the user, builders are not to blame this time unless they didn't cover every bias equally well.

> GPT3 can simulate real people, Jack Clark

https://jack-clark.net/2022/10/11/import-ai-305-gpt3-can-sim...

Ok so GPT-3 can model biases well. This still doesn't solve problems such as the optimal aggregation of citizens' preferences, which is the actual optimization problem of policy making. Just to give an idea of how complex a field this is, there is a subdomain of economic theory and information theory called social choice https://en.wikipedia.org/wiki/Social_choice_theory that works with these issues and itself does not have many theories about policy formation, mostly choice between already formed policies. If a neural net finds the way to write policies that are Nash equilibrium collective decisions in all possible democracy systems then we will be close to solving the problem.

> you can test a neural network a million time [...] You can't do that with a human, you got to take a chance.

Funny, as a biochemist, I always took it that minds had essentially been through unfathomable numbers and levels of iteration through cultural and biological evolution cycles, and that ANNs were the untrustworthy new kids on the block :)

In aggregate, yes, but in particular, it's the opposite.

Been working with and around Polis software for a few years, and have had the chance to visit Taiwan to participate in vTaiwan for a few months. Definitely seconding it as massively interesting.

> they are voting paradigms that focus either only or mostly on how decisions are made but not how the options to decide upon are formed.

Just wanted to second your thought here, as we seem to have converged on this independently. I've been speaking similarly about the framing of Polis. Facilitative processes often require cycles of expansion of possibility and then collapse into singular options (whether the options to vote between, or the yay/nay decision). This expansion/collapse is often called the double diamond process.

For example, civil society discussion is often expansion, deliberative assembly in parliament is also ideally about expansion. Putting forward electorial candidates is expansion. Voting (electorial or parliamentary) is purely collapse. Who participates in either the expansion or collapse is a critical feature that differs in all these.

Different levels of expansion/collapse of possibility space are stitched together in our old process, often built on technology of their era, usually created to suit the era of postal mail and telegraphs (communicating electoral results) and horse-drawn carriage (attending parliamentary proceedings). In its prime, Roberts Rules of Order was a cutting-edge social technology that (coupled with other parliamentary democracy infra), allowed as many people as possible to have their views enshrined in governing law.

Most digital innovation focusses on modernizing the collapse process inherent in voting, but it is the less interesting part imho.

Polis is a tool that involves new technology that potentially unseats Roberts Rules of Order as the universal technology of deliberation (expansion). It's dimensional reduction allows for a lightweight process that accepts TONS of participation. It facilitates larger processes where people can participate more directly in issue-based expansion of the possibilities on the way to collapsing into a singular decision or compromise.

It is a deliberation-support tool (expansion), not a decision-making tool (collapse).

Hope this helps others trying to grapple with the significance of tools like Polis :)

Hit me up on Twitter if this stuff is up your alley: https://twitter.com/patcon_

EDIT: Or there's an unofficial "Polis User Group" discord server for aspiring facilitators or server hosters: http://link.g0v.network/pug

Thank you very much for the nice commment! I like the double diamond process idea. Informative comments like this make me come back to hackernews. I would say that the model that I have been working on is like a framework for both sides. From the few papers that exist about deliberative democracy, the idea is that with a well thought out and inclusive process of expansion on a particular subject, the collapse is essentially useless since its result will essentially confirm the outcome. Polis seems to work quite well with the plurality of opinions but IMO, for the process to be inclusive and for malevolent participants to not hijack the conversation, there needs to exist a set of incentives and disincentives that will make citizens have an honest behavior towards the system. Of course these types of conversations are what I live for so I sent you a message on Twitter :)