Okay, so can AI actually predict elections?
What's a really interesting question?
And this paper you sent over from Brigham Young University
dives right into that.
Yeah, caught my eye.
It's about using something called distribution-based prediction
to forecast election results.
Right, it's using large language models or LLMs
to try and analyze voting trends.
LLMs. Yeah, LLMs.
For those of us who aren't AI experts,
can you break that down a little?
Yeah, so you can think of LLMs as really powerful AI systems
that are trained on tons and tons of text data.
Okay.
And from that, they're able to understand language,
generate text, and even make predictions
based on the patterns they've learned.
So is this basically like instead of trying
to predict how each person will vote,
it looks at broader voting patterns?
Exactly.
It's not about simulating individual voters,
which is really hard and can be like really prone to bias.
It's more about understanding the overall distribution of votes
across different states and demographics.
OK, that makes sense.
So how did they actually test this method out?
Did they use a real election?
They did.
They used the 2020 US presidential election.
They fed the LLM all kinds of data about the election,
so things like demographics, historical voting patterns,
even social media trends.
OK, so what happened?
Was the AI able to predict who won?
The results were surprisingly accurate.
On average, the model's predictions
were only off by less than half a percentage point
for both Biden and Trump and in each state.
Wow, that's really close.
Yeah.
They must have been pretty excited about those results.
Definitely.
I mean, it suggests that this could be a really powerful tool
for understanding and predicting elections.
But like with anything, there are limitations.
Of course, like what?
Well, for one thing, LLMs are always learning and changing,
right?
Yeah.
So their predictions are only as good as the data
they were trained on.
If there's a major shift in public opinion
or, I don't know, some unforeseen event,
then those predictions could be way off.
Right, like a global pandemic, maybe.
Yeah, exactly.
No one could have predicted how COVID would impact
the 2020 election.
That's true.
It's a good reminder that we need
to be cautious about making predictions about the future.
Especially about something as complicated as politics.
Especially about politics.
Absolutely.
Yeah.
So I mean, it's amazing that AI can predict an election
with that level of accuracy.
Yeah, it really is.
And it makes you think about what
this means for the future of AI and how it might impact
politics as a whole.
Totally.
Speaking of the future, did they try
to predict any future elections with this model?
They did.
They used the model to simulate a hypothetical 2024 election
between Trump and Kamala Harris.
Oh, that's interesting.
So who did the AI think would win?
Well, I don't want to spoil the surprise
just yet, but let's just say the results are pretty interesting.
OK, all right.
You've definitely piqued my interest.
But before we get into the 2024 predictions,
can we back up a bit?
I'd love to hear a little more about how this distribution
based prediction method actually works.
Sure.
So one of the key concepts here is
that instead of focusing on individual voters,
it's all about the overall probability distribution
of votes.
Probability distribution.
Yeah, probability distribution.
Can you explain that a bit more?
Sure.
Imagine you're flipping a coin.
You have a 50% chance of getting heads,
50% chance of getting tails.
That's a probability distribution.
In this case, the AI is trying to predict
the probability distribution of votes
for each candidate in each state.
Instead of saying, you know, this person
will vote for this candidate, it's
saying there is a certain percentage chance
that this candidate will win this state.
Gotcha.
So it's like it's a more nuanced way
of thinking about election forecasting.
Yeah, I guess.
It's like you need to account all the uncertainties and stuff.
Exactly.
It's not just black and white.
Right.
It acknowledges that elections are complex.
OK, I think I'm starting to get it.
Good.
But this is all based on historical data.
Right.
Like how do we know that these same patterns will
hold true in the future?
That's a great question.
And it's one of the biggest challenges
for any election forecasting model.
The political world changes constantly.
And there's always unforeseen events,
things that disrupt even the most established trends.
That's a good point.
But it's still really cool to see
how AI is being used to analyze and predict elections.
It feels like, I don't know, we're
entering a whole new era of political forecasting.
It does feel that way.
This research is really just the beginning, I think.
We're probably going to see even more advanced AI models being
developed in the future.
You know, that could completely change how we understand
and predict elections.
OK, so we've talked about the 2020 election.
We've talked about the limitations of AI prediction models.
And we've got this glimpse into the future.
Right.
Now I'm dying to hear what the AI predicted
for that hypothetical 2024 matchup between Trump and Harris.
OK, let's get into it.
What's really cool is that the AI didn't just give one answer.
It generated a whole range of possible outcomes.
And each one had its own probability.
So it's not like a crystal ball where
you get a definite answer.
No, not at all.
It's more like a weather forecast.
It's OK.
Where you see a range of possible temperatures,
you know, precipitation levels.
It's about probabilities, not certainties.
That makes sense.
Yeah.
So walk me through those 2024 predictions.
Like, what did the AI see as the most likely scenario?
Sure.
But first, let me quickly explain what the AI considered
when it was making these predictions.
OK.
So it looked at a ton of factors, things
like historical voting patterns, demographic trends,
even stuff like social media sentiment.
It used all that to create what's
called a probabilistic model of the 2024 election.
So it's not just based on one thing.
It's looking at all these different factors.
Exactly.
It's not just extrapolating from past election results.
Right.
It's more complex than that.
OK.
So are you ready to share what the AI predicted for each
state?
I am.
But are you ready to hear it?
Absolutely.
Lay it on me.
What did the AI see happening in those key battleground
states?
Well, in states like Pennsylvania, Michigan, Wisconsin,
it predicted really tight races.
The margins were so small that it's
almost impossible to say for sure who would win.
So those states are still just as unpredictable as ever.
It seems that way.
The AI also predicted some surprises, though,
in states that were considered safe bets for one party.
Really?
Like what?
Well, in some cases, it predicted
that states that have always voted a certain way
might actually flip to the other party in 2024.
Whoa.
That would be a major shakeup.
What do you think is causing those potential shifts?
It's hard to say for sure.
But it seems like the AI is picking up
on these subtle shifts in demographics and voting patterns.
You know, things that could mean a bigger
realignment in American politics is happening.
Fascinating.
So this distribution-based prediction method,
it's not just about predicting the overall winner.
It's also about understanding these nuances and potential
surprises at the state level.
Exactly.
It gives us a much more detailed view of what's going on.
OK.
So we've talked about the state level predictions.
But I'm still waiting to hear who the AI thinks
will win the whole thing in 2024.
Are you ready to reveal the big prediction?
I am.
But remember, this is just one possible outcome.
Right.
The AI created a bunch of different scenarios.
And this is just the one it considered the most likely
based on what it knew at the time.
OK.
Fair enough.
So drumroll, please.
Who did the AI predict would win the 2024 presidential election?
Well, according to the AI, the most likely scenario is that
Kamala Harris would win the 2024 election
with 303 electoral votes.
Wow.
That's a pretty decisive victory.
But I guess it kind of makes sense, right,
considering some of the state level predictions
we just talked about.
Yeah, for sure.
The model basically showed Harris
at the slight edge in a lot of those battleground states.
And that ultimately pushed her to victory.
So you're telling me AI is predicting a female president
in 2024.
That's what the model suggests.
Oh, that's pretty groundbreaking.
It is.
And I think it's a good reminder that the political landscape
is constantly changing.
What seems unlikely now maybe won't in a few years.
That's true.
We can't forget, though, that this is just one possible future,
right?
A lot can happen between now and 2024.
Absolutely.
AI predictions aren't guarantees.
They're more like tools.
They help us understand how things might play out
and then hopefully make better decisions based on that.
Speaking of informed decisions, did the researchers
just test out Trump versus Harris?
Or did they run this model for other potential matchups, too?
Oh, yeah.
They ran simulations for a bunch of different possible matchups.
They wanted to see how the AI would handle
different political situations.
So I mean, did they find that the AI always favored Democrats?
Or were there some matchups where Republicans came out on top?
That's a really great question.
And it actually brings up one of the biggest challenges
with AI bias.
Bias.
OK, how so?
Well, think about it.
AI models are trained on huge amounts of data, right?
Right.
And that data, it can reflect the biases that
exiled in the real world.
So if the data the AI is trained on
has a certain political leaning, then its predictions
might also have that same leaning.
Exactly.
It's kind of like if you only watched one news channel,
you might start to believe that their perspective is
the only one that matters, right?
Well, AI can fall into that same trap.
It's like an echo chamber effect.
Oh, I see.
So how did the researchers deal with this bias
issue in their model?
They knew it was a big concern, so they took steps
to try and minimize it.
For one, they were super careful about the data
the AI was trained on.
They made sure it included lots of different perspectives,
not just one side of the story.
And they also tested the model on a bunch of different scenarios
to see if it consistently favored one party.
Did they find any bias?
It's interesting, actually.
The AI did seem to have a slight preference
for Democratic candidates in most of the matchups.
So even though they tried to be careful,
some bias still snuck in.
It seems that way.
And it just goes to show how tricky this issue of AI bias is.
There's no easy thing.
I think even if the AI has a little bit of bias,
it doesn't mean its predictors are totally worthless.
Right, exactly.
We should think of these predictions
as more of a starting point.
Something to get us thinking and talking
and to help us figure out where we need to learn more.
So don't take them as gospel, but we can still
learn something from them.
Exactly.
It's about using AI as a tool to help us understand things
better, not to replace human judgment altogether.
Makes sense.
Now, I want to go back to something you mentioned earlier
about how the AI came up with a bunch of different outcomes
for the 2024 election.
Oh, yeah, the range of possible outcomes.
Can you explain that a little more?
Sure.
The researchers didn't just run the model once and say, OK,
we're done.
They ran it thousands of times, each time tweaking
the parameters a little bit.
OK, so instead of saying this candidate will definitely win,
it's like saying, OK, there's a chance this candidate will win,
but here are some other things that could happen.
Yeah, exactly.
That's why this approach is so valuable, I think,
because it acknowledges that there's always
uncertainty in an election.
Right, for sure.
Like, there are so many things that can influence the outcome,
you can never be 100% sure.
Right, so much can change between now and 2024.
Oh, absolutely.
Some big news story, a sudden economic downturn,
even like a celebrity endorsement can totally change things.
It's like, these AI predictions are just snapshots in time.
Exactly, a glimpse into the current state of the race.
Right, not a crystal ball.
Exactly.
Now, you mentioned that they ran the model thousands
of times, each time adjusting the parameters.
Uh-huh.
What exactly do you mean by parameters?
Good question.
So think of parameters as the controls on the AI model.
By adjusting those controls, they
can test out how different factors might affect the election.
So they might adjust the parameters
to see what would happen if voter turnout changed,
or if the economy took a downturn.
Exactly, it's like running different simulations,
seeing how things play out in the model.
OK, that makes sense.
Sounds like a lot of work went into this research.
Oh, yeah, for sure.
But the researchers were really good about being
transparent about their process.
They shared all their code and data online,
so other researchers can check their work.
That's great.
Transparency is always important.
But especially when we're talking about something
as sensitive as election prediction, right?
Absolutely.
It means other experts can look at it,
point out any potential problems,
and hopefully build on it too.
So this research is really just the start
of a bigger conversation about AI and elections, huh?
I think so.
And it's a conversation we need to be having now, you know?
Yeah.
As AI gets more powerful and becomes more common,
we need to understand how it might affect democracy.
Totally agree.
OK, before we move on, I want to circle back
to something we talked about earlier,
that potential for bias in AI models.
Oh, yeah, for sure.
It's super important.
We can't just ignore it, right?
No, we have to actively try to find it and fix it.
So what are some things researchers are doing to try
and combat that bias?
One thing they're doing is being a lot more careful
about the data they use to train the models,
making sure it's representative and doesn't just
repeat existing biases.
So instead of just grabbing data from the internet,
which can be full of all sorts of, you know, bias stuff,
Exactly.
They're looking for ways to get data that's
more balanced and accurate.
Yes.
And they're also working on developing new algorithms,
you know, ones that are more resistant to bias.
OK.
There's a lot of cool research happening there,
and we're starting to see some really promising results.
That's good to hear.
Yeah.
But it sounds like even with those efforts,
dealing with bias in AI is a constant challenge, right?
It is.
It's not a one-time fix.
It takes constant vigilance, you know,
being open about how things work and always questioning
our assumptions.
OK.
That makes sense.
We've covered a lot of ground today.
We've talked about how this new prediction method works,
what the AI thinks about the 2024 election,
and the whole complicated issue of bias.
Right.
Before we wrap up, I'm curious to hear,
like, what are your big takeaways from all this?
What should listeners be thinking about?
I think the biggest takeaway is that AI
is getting really good at analyzing data
and making predictions, even about something
as complex as an election.
But it's not a magic solution.
Yep.
It's not like we can just blindly trust the AI
without understanding, you know, its limitations.
Exactly.
And all those potential biases we talked about.
Exactly.
We got to be skeptical, think critically, and remember,
AI is a tool.
Right.
We need to use it responsibly, not
let it make decisions for us.
Totally.
This research is really fascinating.
And it gives us a glimpse into what election forecasting might
look like in the future.
But it's not a perfect system.
We need to use it in a smart and ethical way.
Exactly.
And as we develop even more powerful AI,
it's going to be even more important to think about things
like fairness, transparency, and accountability.
Absolutely.
Well, before we go, the researchers also
looked at how the size of the AI model affected
how accurate the predictions were.
Oh, right.
That's another interesting part of this.
Can you tell me a bit more about that?
Yeah.
So they experimented with different sizes of LLMs.
Some were small, some were massive, you know,
with billions of parameters.
Remind me, what are parameters again?
Oh, think of them as the building blocks of the AI.
OK.
The more parameters a model has,
the more complex stuff it can learn,
and the more detailed its predictions can be.
So basically, the bigger the AI, the better the predictions.
In a lot of cases, yeah.
They found that the bigger models were usually more accurate.
They could see those subtle trends and patterns
that the smaller models missed.
OK.
That makes sense.
Kind of like having a bigger, more powerful telescope.
Exactly.
And as we keep making bigger and better AI,
who knows what they'll be able to predict?
Maybe one day they'll be able to predict elections
with perfect accuracy.
It's definitely possible.
But even then, we'll still need to be careful about those biases
and use AI responsibly.
Good point.
Well, I think we've covered just about everything.
Yeah, I think so too.
Any last thoughts before we wrap up?
Just that this research is really exciting.
It shows us what's possible with AI
and how it can help us understand the world.
But it's a fast-moving field, so we
need to stay informed and keep learning.
I couldn't agree more.
Thanks for joining me today.
It's been a really great discussion.
My pleasure.
It's always fun to dive into these topics.
OK, folks.
That's all the time we have for today.
We hope you enjoyed this deep dive into AI and election
prediction.
Stay tuned for more exciting episodes.
So we've talked a lot about how AI might predict elections.
But you mentioned something earlier, Silicon Sampling,
that the researchers decided not to use for this.
Why'd they scrap that approach?
So at first, they thought about using Silicon Sampling
to create these simulated voters, almost like building
a little world of AI citizens to cast votes.
But they realized pretty quickly that this method was just
it was too likely to result in stereotypes.
So instead of capturing how complex real voters are,
the AI was just making these broad generalizations.
Exactly.
Like, imagine it predicting that everyone
from a certain area with a certain religion
would vote the same way.
That's obviously way too simple, right?
It doesn't show how different people's opinions can
be within any group.
Right, like saying all cat owners vote the same way.
Because I don't think it doesn't make sense.
People's votes depend on so many things.
Other beliefs, experiences, values.
It's way more than just demographics.
Exactly.
And the researchers, they understood that.
They saw that simulating individual voters like that
was just too risky.
It could lead to predictions that were biased and misleading.
So they decided to focus on this distribution-based
prediction instead.
Right, which is a much better approach, I think.
Yeah, it seems a lot more robust.
It is.
Because instead of getting stuck trying
to simulate every single voter, they're
looking at the bigger picture.
They analyze the patterns in the data,
which is much less likely to fall into those stereotypes.
It's like instead of trying to track every single raindrop
in a storm, you're looking at the direction
the storm is moving, how strong it is,
the overall picture.
That's a great analogy.
And it kind of highlights something important about AI,
I think.
That's a app.
We can't just blindly trust it.
We've got to question our assumptions,
make sure we're not just repeating the biases that
are already out there.
Absolutely.
Especially when it comes to something
as important as elections.
We can't just let the AI make the decisions for us.
You have to be thoughtful about it.
Exactly.
Understand the limitations and be critical of the results.
So this research, it wasn't just about finding a new way
to predict elections.
It was also about using AI responsibly and ethically.
Exactly.
It's a great example of how researchers are starting
to think about these things.
And how to make sure AI is developed in a way that's fair.
Yeah.
It's a powerful tool, but it's still a new tool, right?
It is.
We're still figuring out how to use it properly.
And the more powerful AI gets, the more important
it's going to be to keep having these conversations
about how to use it ethically.
OK, so as we wrap up here, what are your main takeaways
from this research?
What do you want listeners to walk away thinking about?
Well, I think the big thing is that AI is becoming really
good at analyzing information and making predictions,
even about something as complicated as elections.
But like we've been saying, it's not a magic crystal ball.
We have to be careful about those limitations and biases.
Absolutely.
Be critical.
Ask questions.
This research is a fascinating look
at what might be possible, but it's not a perfect solution.
We need to be thoughtful and responsible with this kind
of technology.
Well said.
And as we keep developing even more powerful AI,
fairness and transparency are going
to be more important than ever.
Totally agree.
Well, that's all the time we have for today's Deep Dives
into AI and election prediction.
It's been great talking about this with you.
Thanks for joining me.
We hope you learned something new and interesting today.
And remember to stay curious, stay informed,
and keep those conversations about the future of AI going.