> ChatGPT has a nice clear style and will probably help make these laws more accessible.

If the laws are not meant to be read in the first place (which I agree with as a likely pattern), how is this motivating? I'm skeptical that they're not read because of their density. I'd flip the causality; they're dense because they're not to be read.

> nice neutral way of arguing about what is in a bill

Elon Musk is already talking about "TruthGPT", an AI without "liberal bias". I don't think you will find anything resembling neutrality in this conversation.

Between TruthGPT, Chuck Schumer & Mitch McConnell I would expect the most honest summary to be TruthGPT. I doubt it'd be close. Even with a very healthy dose of cynicism towards all billionaire projects and scepticism of the abilities of AI. If nothing else the AI has a big advantage because it would have read and possibly remembered the text of the bill and could answer specific questions which already gives it a massive edge over its geriatric competition.

The politicians lie proactively with plausible deniability and/or people wilfully ignoring it because of team dynamics. Something like ChatGPT is hard to train to lie without it being demonstrable with experimental evidence.

See, I consider the functional definition of what current generation LLMs do to be "lying". Not out of malice or moral failing (of the model or the model's creators), but simply out of lack of control/alignment/modulation systems that people typically associate with honesty, integrity, and intention.

The problem-model behind these systems is "predict what comes next". The objective in training is not accuracy of content (by whatever definition...), but verisimilitude of output. An LLM happily improvises on whatever seed you give it. Sometimes this leads to output that aligns with someone's definition of reality. Sometimes it's hallucination or bullshit or garbage or whatever you term you want to use for it. You can modulate this with better prompt engineering, but only by degrees.

I have no doubt future generations of these systems will start tackling this, but the systems as they stand now are by their very nature, liars.

edit: And additionally, while I can't necessarily argue against your trust in these systems (especially given the choice you presented.. I'd probably choose XGPT too), "neutrality" is defined by a shared trust that I don't see a route to in the current climate.

ChatGPT "lies" even when you don't want it to. It's true that it's not a strategic liar like a regular politician, and liable to give the game away by accidentally telling the truth, but it'll happily refine your lies or make up coherent nonsense pretty much anywhere it hasn't been trained specifically not to

Since “TruthGPT” has an overt mission (“anti-woke”) to lean even harder into biases existing LLMs have demonstrated, I... would expect the opposite.

> Something like ChatGPT is hard to train to lie without it being demonstrable with experimental evidence.

ChatGPT lies all the time, and has no innate access to experimental evidence.

I'd find it interesting to chat to an LLM which had pre-training and fine-tuning for instruction-following, but no attempt to train it to be "safe" or "unbiased" or "truthful". Of course, it is going to repeat whatever biases exist in its pre-training data – but it would be an interesting way of exploring what those biases actually are.

I wonder if Musk has thought of using the algorithm of Twitter Community Notes (formerly Birdwatch). Essentially, its algorithm treats a claim as true when people who normally disagree on what is true agree that it is true. If people at opposite ends of the political spectrum agree X is true, it probably is true, and its truth is unlikely to be contested or controversial. So you could train a LLM to endorse those kind of non-controversial truths. When it comes to the opposite kind of statement, the kind of highly controversial claim on which there is widespread disagreement, there are two possible strategies: (1) train it to avoid taking sides, (2) don't do anything and just let it answer according to the biases of its pre-training data, whatever that may be.

That way you could train an LLM to not be "woke" (or at least no more "woke" than its training dataset is), without training it to be "anti-woke". It would be interesting to talk to such an LLM and see what kind of opinions it may endorse.

That said, I can only really agree that it might be a net good IF the weights (and all other data necessary to reproduce the summaries) were open knowledge. Without that, you are just shipping a bill off to a black box and trusting that the gremlins in the box are acting in good faith. If every legislative office is doing this, then those gremlins will become the new focal point of a basically-invisible form of lobbying. This is good for OpenAI (and whoever controls it) but probably bad for everyone else.

Can it, though? The context window is nowhere near 4,000 pages. Getting ChatGPT to understand and summarize such a long document seems nontrivial.

But expecting people to read 4000 pages in 3 days and make informed decisions after that is unrealistic, regardless of whether they are using lossy untrusted compression tools like ChatGPT.

(AIO sounds less catchy)

Or make it worse, because it completely normalises the practice of foisting long revisions on people at the last minute "you had plenty of time to understand it; my computer read it in seconds!" and encourages people rely more on tooling than dividing reading text between themselves and people they trust who (unlike GPT) have a shared interest in achieving particular goals and protecting particular parties.

From what we've seen current gen summarising tools aren't particularly accurate at summarising legal documents, never mind highlighting stuff which is politically sensitive, and that's before we get into clauses inserted adversarially by entities who have the same tools to test whether GPT summaries pick them up

So can the legislative staff of the committee of responsibility.

> And it'll be a nice neutral way of arguing about what is in a bill - people might be able to agree on which AI is reasonable

Ha ha ha ha. No. Just like they can’t do that with human experts, and for the same reason. Heck, this has already been demonstrated in the case of AI models quite resoundingly.

And it would be a bad thing if they could: designating a single AI vendor as the sole arbiter of truth in the lawmaking process would be much worse than the status quo.

We're going to wind up with ChatDNC and ChatRNC. Nobody in office is going to agree on which one is reasonable, for the same reason that they refuse to agree on anything today.

It's more likely that they'll further politicize these models.

Congress is similar to most other large organizations, such as large software companies. In a large company software company the upper level managers don't actually read all the code. They make their decisions based what managers below them report, and so on down the management chain.

Representatives and Senators are Congress' equivalent to those upper level managers.

The House has over 6000 staff members, ranging from staff working for individual representatives to staff employed by committees. The Senate has over 4000 staff members. Those are the people that deal with nitty gritty details of the bills.

There's no world in which you'll ever get an organism to know everything top down.

So I have to assume the remaining conclusion you have is a naive understanding of organizational structure and super organisms.