Welcome to our deep dive today.
We're gonna be exploring artificial intelligence
for social good.
Oh, very interesting.
Specifically in justice systems.
Oh yeah?
We've got this source material called
artificial intelligence for social good principles.
I see.
And honestly, I'm pretty curious to see if AI
can really live up to this promise of justice
without borders.
Yeah, what do you think?
I mean, it's a powerful idea.
It is this vision of a world where justice
is truly accessible to every single person.
Right, but can AI really deliver on that?
Well, that's the big question, isn't it?
Yeah.
I think to figure that out, we really
need to understand both the potential and the pitfalls.
For sure.
Absolutely.
And this document kind of lays out a framework
for how AI can be used to benefit society.
But it also seems to kind of hint
at some of the challenges involved.
Yeah, for sure.
So what are your initial thoughts on all this?
Well, one thing that really stands out to me
is this whole idea of AI as a double-edged sword.
It's got this potential, right, to address some of those
systemic inequalities in our justice systems.
But it also has this risk of making those inequalities worse
if we're not super careful.
Yeah, if we don't design and implement these systems
responsibly.
Exactly.
OK, so this is definitely a complex issue.
It is.
So where do we even begin with all of this?
The document mentions these four key principles, equity
and inclusion, accountability and explainability,
human centricity, and then global collaboration.
Yeah, those are important.
So how do all of these principles actually
play into this whole justice without borders idea?
Well, I think those principles are absolutely
central to this whole discussion,
like let's take equity and inclusion as an example.
A justice system can't really be just
if it doesn't actually serve everyone equally, right,
regardless of their background or their circumstances.
Yeah, for sure.
And that's where AI can potentially
make a big difference.
Just imagine AI-powered tools that
can provide legal assistance in hundreds of languages,
making legal services accessible to communities
that have been historically marginalized
or just totally excluded.
That would be incredible.
Right.
It's like taking those justice without borders aspirations
and making them a reality.
Exactly.
But isn't there a risk that these AI systems could actually
perpetuate existing biases?
Oh, for sure.
I've heard some concerns about algorithms
being used in criminal sentencing that end up
discriminating against certain groups.
Yeah, you're right to raise that concern.
OK.
That's exactly where the principle of accountability
and explainability comes into play.
OK.
If we're going to be using AI in these high-stakes situations,
we need to be able to actually understand
how they are making decisions.
So we need to be able to hold them accountable.
Yes, exactly.
We can't just blindly trust these algorithms.
We need what's called glass box AI.
OK, because.
Where that decision-making process is totally transparent
and open to scrutiny.
So instead of relying on this mysterious black box,
we need to be able to look under the hood
and see how the engine is actually working.
Exactly.
But how do we actually achieve that level of transparency?
Well, there are different techniques
you can use to make AI more explainable.
Like, for example, instead of relying
on these deep learning models, which can be really opaque,
we can use rule-based systems or decision trees, where
the logic behind each decision is super clearly laid out.
Right, so it's not just about building systems that work.
It's about building systems that we can understand.
And trust.
Yeah, and trust.
That's essential.
Especially when we're talking about something as fundamental
as justice.
Absolutely.
So we're not just talking about handing over the gavel
to a robot judge.
No, not at all.
The goal here is to use AI to augment human judgment,
to give judges and lawyers more information, better insights,
so that they can make fairer and more informed decisions.
I can see how that would be really valuable,
especially in complex cases, where there's just so much data
to go through.
Definitely.
But what about the potential for bias?
Even if humans are still making the final call,
couldn't those biased algorithms still influence their judgment?
Yeah, that's a totally valid concern.
And it really just highlights the need
for that careful design and testing of these AI systems.
We need to be constantly evaluating,
auditing these algorithms.
To ensure that they're not perpetuating or amplifying
those existing biases.
Exactly.
And that brings us back to that principle of equity
and inclusion.
It's not just about making sure the algorithms are unbiased,
but that they are actually being used in a way that promotes
fairness and justice for everyone.
100%.
And that's precisely where the principle of global collaboration
comes in.
This isn't just a challenge for one country or one region
to solve.
We need to be working together on a global scale.
To develop ethical guidelines and standards
for how we use AI in these justice systems.
I'm really starting to get a sense of just how interconnected
all of these principles are.
Yeah, they all play together.
It's not enough to just focus on one or two.
We need to be thinking about all of them simultaneously.
Yeah, if we want to create a truly just and equitable system.
Exactly.
So we've talked about this whole idea of AI as a double-edged
sword.
And we've explored those four key principles that
can kind of help us navigate this complex landscape.
But how do these principles actually
play out in real world situations?
Well, let's look at some examples.
Yeah, let's dive into some concrete examples
of how AI is being used in justice systems today, both
for good and maybe not so good.
Sounds good.
One area where AI is already making a big impact
is in criminal sentencing.
Oh, yeah.
You've got algorithm-like compass that
are being used to try to predict the likelihood of someone
reoffending, which judges can then consider
when making sentencing decisions.
But there's been a ton of debate about whether these tools
are actually fair and unbiased.
Yeah, compass is a really, really interesting example
of how AI can both promise this greater fairness,
but also raise some serious concerns about bias.
On the surface, using an algorithm to assess risk
seems like it could make the system more objective.
Because human judges, I mean, we know,
they can be susceptible to all sorts of biases,
conscious and unconscious.
But here's the problem.
These algorithms are often trained
on data that reflects the existing biases
in the criminal justice system.
So even if the algorithm itself is neutral,
the data it's fed can kind of bake in those biases,
leading to unfair outcomes.
Exactly.
So it's not enough to just create
like a technically sophisticated algorithm.
You also have to be incredibly careful about the data
that's used to train it.
You got it.
And even then, predicting recidivism,
that's inherently difficult, right?
It really is.
There are so many factors that contribute
to whether someone will reoffend,
a lot of which are impossible to really capture in a data set.
Yeah.
And to rely on those predictions for something
as consequential as sentencing, I mean,
it raises some really profound ethical questions.
It makes you wonder if we're maybe putting a little too
much faith in these algorithms, especially given
that potential for error and bias.
Definitely.
That's why this human in the Luke model is so important.
It is.
These cools can provide valuable insight.
They can.
But in the end, the final decision
really should rest with a human judge
who can consider all of those nuances of the case.
You got it.
Speaking of nuances, let's maybe shift gears a little bit.
And talk about access to justice.
Huge issue globally, right?
It is.
Millions of people just lack access
to even basic legal services.
Yeah, that's right.
So can AI actually help bridge that gap?
Absolutely.
There are already these AI-powered platforms
that are making legal information and assistance
more accessible, especially for underserved communities.
Just imagine chatbots that can answer
those basic legal questions, guide people
through legal processes, maybe even help them draft
simple legal documents.
Wow.
These tools can really empower individuals
to navigate the legal system with more confidence
and understanding.
It's like having a virtual lawyer in your pocket.
Right.
That could be huge for people who can't afford
traditional legal representation.
Absolutely.
And it's not just about individuals either.
AI can also help level the playing field
for small businesses.
Imagine a contract analysis tool that
can help a small business owner actually understand
the terms of a complex contract without having
to hire expensive lawyers.
That kind of access to legal expertise
can make all the difference for a struggling entrepreneur.
It seems like AI has this potential to democratize
access to justice in a way that just wasn't possible before.
I think so.
But are there downsides to these tools?
Well, one challenge is just making sure
that these tools are designed in a way that's truly
accessible to everyone.
For example, if a legal chatbot is only
available in English, it's not going
to be very helpful to someone who speaks Spanish.
Yeah, or Mandarin.
Exactly.
We need to be prioritizing those AI tools that
are multilingual and culturally sensitive.
It's like we were saying earlier about equity and inclusion.
You can have the most advanced AI in the world.
Doesn't matter.
But if it's not actually accessible to everyone?
Right.
It's not really serving its purpose.
Exactly.
And it's not just about language either.
We also need to consider the digital divide.
These tools are only effective if people actually
have access to the technology.
And the internet.
Yeah, the internet connectivity.
They need to actually use them.
So it's not just about building the tools.
It's about creating a whole ecosystem.
A whole ecosystem.
Where everyone can benefit.
Exactly.
And that's where that principle of global collaboration
comes in again.
We need these international organizations,
governments, tech companies.
All working together.
All working together to make sure
that these AI-powered justice tools are
available and accessible to everyone.
To everyone.
Regardless of location.
Or socioeconomic steps.
Right.
So achieving justice without borders
requires this multi-pronged approach.
A multi-pronged approach.
Addressing both the technical challenges
of developing the AI itself.
Right.
And then also the social and economic barriers
that prevent people from accessing justice.
Absolutely.
But even if we overcome all of those hurdles,
there are still some fundamental challenges
that we really need to grapple with.
OK, let's talk about that.
The document highlights three key challenges.
OK.
Resource gaps across countries.
Uh-huh.
The lack of universally agreed upon ethical frameworks.
Right.
And then the difficulty of establishing accountability
for these AI systems.
Yeah, those are some complex issues.
They are complex.
That are going to require some really thoughtful solutions.
Very thoughtful.
So let's break those down one by one.
OK.
What do you mean by resource gaps across countries?
Well, developing and deploying these AI systems,
it requires significant resources, expertise, data,
computing power, infrastructure.
These resources are not evenly distributed across the globe.
OK.
You know, a wealthy nation might have the capacity
to create this sophisticated AI-powered justice system.
Right.
But a developing nation might struggle
to even just get basic internet access to its citizens.
So it's not a one-size-fits-all approach
to AI for social good?
Definitely not.
OK.
We need solutions that are tailored to the specific needs
and contexts of different countries and communities.
A justice AI system that works well in the United States,
you know, might be completely inappropriate for a country
with a very different legal system or cultural norms.
It's like trying to export a legal system from one country
to another.
Exactly.
It just doesn't translate well.
It doesn't translate well.
And that leads us right into that second challenge.
OK.
This lack of universally agreed upon ethical frameworks
justice is this really complex concept
that's shaped by culture, history, values,
what's considered fair and just in one society
might be seen as totally unacceptable in another.
So how do we create ethical guidelines for AI
and justice systems?
That's the big question.
When there's so much diversity in how different cultures define
justice.
It's a great question.
It's going to require a lot of dialogue
and collaboration between experts from different disciplines
and cultural backgrounds.
It won't be easy to find that common ground.
For sure.
But it's absolutely essential if we
want to create AI systems that are truly ethical and just
on a global scale.
I can see how that would be a very delicate process navigating
all those cultural differences.
But let's say we do actually manage to agree
on some basic ethical principles.
How do we ensure that AI systems are actually
adhering to those principles?
That brings us to that third challenge you mentioned.
The difficulty of establishing accountability.
Yeah, this is where things get tricky.
AI systems are complex.
And they're decision making processes.
They can be opaque.
It can be difficult to determine why
an AI system made a particular decision.
And even harder to actually assign blame
when something goes wrong.
It's like trying to hold a shadow accountable.
How do you begin to address that?
Well, one approach is to really focus on transparency.
The more we understand about how these systems work,
the better equipped we'll be to identify potential biases
or errors.
And we need to develop mechanisms
for auditing these systems, both during their development
and after they've been deployed.
So it's about building in safeguards
from the very beginning.
And then having that ongoing oversight
to make sure things are actually working the way they're
supposed to.
Exactly.
And when things do go wrong, we need
these clear lines of accountability.
Who is responsible when an AI system makes a mistake?
That harms someone.
Is it the developer who created the algorithm,
the company that deployed it, the government agency that's
actually using it?
Those are tough questions.
They're tough questions.
It's like a legal and ethical maze.
It is a maze.
And there aren't easy answers.
But these are conversations that we
need to be having right now as AI just becomes more prevalent
in our justice system.
We can't wait until it's too late.
We can't wait.
We need to be thinking about these issues proactively,
rather than waiting for something to go wrong.
Right.
And then trying to scramble for solution.
Exactly.
It sounds like there's a lot of work to be done.
A lot of work.
Both in terms of refining the technology itself
and in developing the ethical and legal frameworks
to guide its use.
But despite all of these challenges,
it seems like there's still this sense of optimism
in the document, right?
This belief that AI can ultimately
help create a more just world.
I think that optimism comes from recognizing
that AI has this potential to address some of the most
like deeply ingrained problems in our justice systems.
Problems that have been around for generations.
We have a real chance here to create a system that
is truly fair and equitable.
A system that upholds the rights and dignity
of all individuals.
Yes, exactly.
It's like we're standing at a crossroads
with the opportunity to choose a path toward a brighter future.
We have a choice to make.
That path won't be easy, though.
It's going to require vision, courage, and a willingness
to challenge that status quo.
We have to be bold.
We have to be bold in our aspirations,
while also remaining rounded in the realities of what's
possible.
And we need to be constantly questioning, testing our
And always striving to do better.
So it's not just about developing the technology.
It's also about having the wisdom to use it responsibly.
And ethically, and what wisdom comes from understanding
the complexities of the human experience,
the nuances of justice, and the potential consequences
of our actions.
Right.
We need to approach AI with a sense of humility.
Humility.
Recognizing that it's this powerful tool that
can be used for good or for bad.
And it's like we said at the beginning, right?
AI is a double-edged sword.
It is.
And it's really up to us to decide how we wield it.
That's right.
Wow.
We've covered a lot of ground in this deep dive.
We have.
From the potential of AI to totally revolutionize access
to justice, to those very real risks of bias.
And those big challenges of creating a global framework
for ethical AI development.
Yeah.
It's a lot to think about.
It's clear that this technology is rapidly evolving.
It is.
And its impact on our legal systems,
it's only going to grow in the coming years.
It's an exciting time to be thinking about these issues.
We're at this pivotal moment where the choices we make today
are really going to shape the future of justice.
Yeah, for generations to come.
And that brings me to something I've been kind of pondering
throughout our conversation.
What role do individuals, like our listener, play in all of this?
It's easy to feel like these are massive complex issues that
are just totally beyond our control.
But as we've been discussing AI development,
it's not happening in some vacuum.
It's not.
It's shaped by the values.
And the priorities of the people who
create and deploy these systems.
Absolutely.
Which means that we, as individuals,
we have a responsibility to engage with these issues.
We do.
To ask those tough questions.
Yeah.
And to demand that AI is used in a way that benefits all
of humanity.
100%.
We can't just passively accept whatever
technological solutions are presented to us.
We need to be informed, critical thinker.
We need to be able to discern those potential benefits
and risks of AI.
And we need to be vocal advocates for the kind of future
that we want to see.
So what can our listener actually do?
Yeah, what can they do?
Practically speaking, to actually make
a difference in this space.
Right, because it can feel daunting.
It can.
And know where to even begin.
Yeah.
A great first step is honestly just to stay informed.
It's OK.
Read articles.
Listen to podcasts like this one.
Right.
Engage in those conversations about AI
and its impact on society.
Right.
The more you understand about these issues,
the better equipped you'll be.
Yeah.
To participate in the debate.
And to make informed choices.
And even of informed choices, you
mentioned earlier some resources that we'd share.
Oh, yeah.
Those are a great place to start.
Definitely.
We'll be sure to include links to some valuable resources
in the show notes for this episode.
Perfect.
But it's not just about acquiring knowledge.
Right.
It's also about actually putting that knowledge into action.
Exactly.
There are so many ways to get involved.
You can support organizations that
are working to promote ethical AI development.
Right.
You can contact your elected officials.
Right.
Urge them to prioritize these issues.
Yeah.
Even something as simple as just having conversations
with your friends and family about AI and its implications
can help raise awareness.
Yeah.
And to mark that critical thinking.
Those everyday conversations can be super powerful.
They can.
They challenge assumptions.
They shift perspectives.
Yeah.
And they inspire people to take action.
That's right.
It's this reminder that we're not just
like these passive consumers of technology.
Right.
We have a voice.
We do.
And we can actually use it to shape the future.
100% and don't underestimate the power
of your individual choices.
OK.
As consumers, we have a say in which companies we support
and which products we use, we can choose
to invest in those companies that
are committed to ethical AI development.
Yeah.
And we can demand that transparency and accountability
from the companies we do business with.
It's a reminder that even small actions
can have a ripple effect.
They can.
By making those conscious choices in our everyday lives.
Yes.
We can send a message that we care about these issues.
Exactly.
And that we demand better.
We demand better.
From the companies that are shaping our technological future.
That's right.
And as we wrap up here, I want to bring us back
to that vision of justice without borders.
Yeah.
It's a bold, ambitious goal.
It is.
But it's one that's absolutely worth striving for.
Right.
And while it may seem far off, I believe that by working
together, by embracing those principles we've
talked about today, and by demanding better for ourselves
and from those companies and institutions that
are wielding these powerful technologies,
we can create a world where justice is truly accessible to all.
It's been such a thought provoking conversation.
It has been.
Thank you so much for joining us on this deep dive.
Thanks for having me.
And to AI for social good and justice systems.
It's been great.
We hope you found it informative, engaging,
and maybe even a little bit inspiring.
I hope so.
And remember, this conversation doesn't end here.
It doesn't.
Keep exploring, keep questioning,
and keep pushing for a future where technology serves
humanity.
Yes.
Not the other way around.
Well said.
Thank you for listening.