Okay, so get this, today we're diving into AI, right?
But there's a twist, something you might not see coming.
Are we gonna be talking about politics?
Politics and AI.
I know, right?
Sounds a little crazy.
But stay with me, okay?
We're going deep on this research paper,
straight out of MIT.
It's called On the Relationship Between Truth
and Political Bias in Language Models.
Yeah, it's a fascinating paper, really makes you think.
It does, it really does.
So the researchers were trying to make AI more truthful,
which on the surface sounds like a good thing, right?
Absolutely, yeah.
But what they found was that by doing that,
they might actually be making these AI systems
more politically biased.
Wait, hold on, so making AI better at spotting facts
and stuff could actually make it more biased.
That's kind of a big deal.
It is a big deal, yeah.
But before we go any further, right,
let's make sure we're all on the same page here.
For anyone listening who's maybe not super familiar
with AI, what exactly is a language model?
Hmm, okay, good question.
So think of it this way.
A language model is kind of like that auto-complete
on your phone, but supercharged.
It's an AI system that can predict what word
comes next in a sentence,
which is pretty amazing when you think about it.
This lets it do some cool stuff,
like write different kinds of texts, translate languages,
even answer your questions.
It can even sound pretty human sometimes.
And it does all this by learning from massive amounts
of text data, like a ton of information.
So it's basically like gobbling up all this info
and then figuring out how to use language like we do.
That's pretty wild.
It is pretty wild.
Okay, but back to the research, right.
Why are these scientists so focused on making AI truthful?
Like why is that so important?
Well, imagine like getting medical advice from AI
or reading news articles written by AI.
Oh yeah.
You'd wanna be sure it was accurate, right?
Absolutely.
That's why truthfulness is a big focus in AI right now.
We need to know we can trust these systems
to give us reliable information.
Yeah, no, that makes total sense.
So how did the researchers actually teach these AI models
to know what's true and what's not?
Okay, so they use something called reward models.
Imagine you're like training a dog
and you give it a treat when it does something good.
Reward models kind of like that.
It gives higher scores to statements that seem true
and lower scores to ones that seem false.
So it's basically like encouraging the AI
to figure out what's true, right?
Exactly, exactly.
Pretty clever.
So I'm guessing they didn't just use any information
to train these models though, right?
Right, they were very specific about it.
They used all these data sets full of truthful statements,
scientific facts, stuff from Wikipedia.
They even used tricky questions like,
designed to see if the AI could spot a lie.
Wow, they really covered all the bases.
Sounds like they really do their homework.
So drumroll please, did it work?
Were the AI models able to tell the truth
from what wasn't true?
Well, they definitely got better
at identifying true statements, that's for sure.
But here's where things get interesting.
They found that a lot of these AIs,
the ones that were supposedly good at spotting truth,
started showing a pretty clear political bias
and it was mostly leaning to the left.
Whoa, hold up, are you telling me
that by trying to make AI more truthful,
it somehow became politically biased?
How is that even possible?
Yeah, that's the million dollar question.
To measure this bias,
they used this clever data set they made called TwinViews.
The weight views.
Yeah, it's got thousands of these pairs of statements
on all sorts of hot button issues,
you know like climate change, LGBTQ plus rights.
But here's the thing,
each pair presents opposite views,
one leaning left and one leaning right.
Oh, okay, I see.
So they're like putting these politically charged
statements head to head and then seeing which side
the quote unquote truthful AI picks.
That's pretty smart.
But how they make sure those statements
actually matched up with real world political views.
Okay, so they used another AI for this GPT 3.5.
They basically told it to create statements
that aligned with either left or right leaning views.
So they were grounded in like real world political ideology.
So they used AI to build the tool
to test for bias in other AI.
Exactly.
That's wild.
And what happened?
Did the truthful AI show a preference?
Like consistently for one side or the other?
Yeah, it did.
In most cases, the AI rated the left leaning statements
as more truthful.
And the bigger the AI model,
the stronger this bias seemed to be.
Okay, so now we've got a real mystery on our hands.
If these AI's are learning from like, you know,
factual information,
where's this political bias coming from?
Right, it's a head scratcher.
The researchers were stumped too.
First they thought maybe it's in the data they used
to train the AI.
Yeah, that makes sense.
But they looked really closely
and there wasn't much political content there.
So it's not like they were feeding the AI
a bunch of political stuff, you know, to warp its view.
Nope, not at all.
Then they thought, okay,
maybe there are these hidden clues
in how the statements are written.
Hidden clues.
Yeah, like maybe truthful statements
tend to use certain words or phrases
that are also more common in, you know,
left leaning language.
Oh, so the AI might be picking up on those patterns
even if we don't notice them.
Exactly.
But to test that,
they used a simpler model
that just focused on word patterns
and they didn't find anything conclusive.
So no sneaky clues there.
So we've ruled out the training data
and we've ruled out these sneaky clues in the language.
That leaves us with a pretty big question mark.
It does.
The researchers basically said
this bias might be coming from somewhere else entirely,
something we haven't even thought of yet.
They said they need to do a lot more research
to figure it out.
Yeah, sounds like it.
This is turning into a real AI detective story.
It is, it really is.
So it's like we've got this truthful AI
but it's showing this political slant
and we don't really know why.
It's kind of freaky, you know?
Yeah, it definitely raises some questions.
It just shows how complex these AI systems really are.
Like we're only beginning to understand
how they learn and process information
and this research, it's like,
whoa, there's a lot more going on than we thought.
Yeah, it makes me wonder if there are other
like hidden biases in these systems.
Things we haven't even discovered yet.
Kind of scary actually.
It is a little bit, yeah.
But going back to this political bias thing,
did the researchers find it was like across all topics
or did it show up more in some areas than others?
That's a good question.
They found it was strongest with topics like climate change,
renewable energy and labor unions, issues
where there's already a lot of debate
and division in the real world.
Right, right.
So it's like the AI is picking up
on those existing tensions
and then somehow reflecting them
in how it judges truth.
That's pretty interesting.
Did they find any topics where the bias was like less noticeable
or maybe even flip the other way?
They did actually.
They found it was weaker or even reversed
on topics like taxes and the death penalty.
In those cases, the AI actually seemed to lean more
toward conservative views.
Huh, so it's not just like always leaning left.
There's more to it than that.
This is getting more and more interesting.
It is, yeah.
But you know, regardless of where it's coming from
or which way it leans, this bias thing
has some pretty big implications for AI.
Don't you think?
Oh, absolutely.
If we want AI systems we can really rely on
for accurate information, information that's not biased.
We need to understand how this bias works
and how to stop it from getting into the system.
We might even have to rethink how we train AI completely.
So it's not just about feeding AI more data.
It's about being aware of these subtle ways bias can creep in
even when we're trying to be objective about it.
It really makes you wonder if we can ever create
a perfectly objective AI.
Yeah, I mean these systems are learning from data
made by humans and we're all biased in some way, right?
True, true.
It's like, are we accidentally putting our own biases
into these AI systems?
And if so, what does that mean for, you know,
how AI will be used in the future?
Big questions, for sure.
We can't ignore them, especially now that AI
is becoming so, so integrated into our lives.
Think about it, we're already using AI for things like
hiring, loan approvals, even the criminal justice system.
If those systems have hidden biases,
that could have a huge impact on people's lives.
Exactly, and it might not always be fair.
Right.
And these biases, they might not always be as clear as,
you know, a political leaning.
They could be about gender, race, religion,
all sorts of things.
It's a lot to think about.
It is.
So what does this all mean for, you know,
everyone listening, the people using AI every day?
What's the takeaway here?
I think the biggest thing is awareness.
We have to remember that even the smartest AI systems,
they're not perfect.
They can be influenced by bias, just like us.
Even when we're trying our best to make them objective.
So you can't just, like, blindly trust
everything AI tells us.
Exactly, don't be afraid to question the information,
you know, look into it a bit more.
Think about other perspectives,
just like you would with any other information.
That's good advice.
Yeah, it's definitely a lot to consider.
I mean, we're trying to use AI to find truth,
but it turns out even that can be kind of messy.
I guess, like you said, it just shows how complex
all this stuff really is.
It really does.
So as we're wrapping up this deep dive,
what's the one thing you really hope our listeners
take away from this?
What's that final thought they can keep in mind
as they, you know, navigate this whole AI world?
Okay, so next time you're interacting with an AI system,
a chatbot, a search engine, whatever,
just take a second to think about the data it was trained on.
Okay, yeah.
Like who put that data together, what their goals were,
and think about whether there might be some,
you know, unconscious biases
shaping the information you're getting.
That's a really good point.
We should always be thinking critically
about where information comes from,
whether it's from a person or an AI.
Exactly, it's a fascinating topic,
and this research, it's just scratching the surface,
but by being aware of these potential biases,
we can start using AI in a more responsible way.
So it's not about being afraid of AI, right?
It's about understanding it,
questioning it, and using it to make things better.
Exactly, AI has so much potential to do good,
but it's up to us to make sure it's developed
in a way that benefits everyone.
Couldn't have said it better myself.
That's a perfect note to end on.
We need to approach AI with like a healthy mix
of curiosity and caution, recognizing its power,
but also being aware of the potential downsides.
Thanks for joining us for this deep dive into AI and truth.
Until next time, keep exploring, keep questioning,
and stay curious out there.