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Welcome to our deep dive today.

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We're gonna be exploring artificial intelligence

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for social good.

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Oh, very interesting.

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Specifically in justice systems.

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Oh yeah?

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We've got this source material called

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artificial intelligence for social good principles.

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I see.

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And honestly, I'm pretty curious to see if AI

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can really live up to this promise of justice

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without borders.

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Yeah, what do you think?

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I mean, it's a powerful idea.

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It is this vision of a world where justice

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is truly accessible to every single person.

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Right, but can AI really deliver on that?

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Well, that's the big question, isn't it?

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Yeah.

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I think to figure that out, we really

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need to understand both the potential and the pitfalls.

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For sure.

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Absolutely.

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And this document kind of lays out a framework

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for how AI can be used to benefit society.

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But it also seems to kind of hint

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at some of the challenges involved.

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Yeah, for sure.

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So what are your initial thoughts on all this?

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Well, one thing that really stands out to me

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is this whole idea of AI as a double-edged sword.

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It's got this potential, right, to address some of those

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systemic inequalities in our justice systems.

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But it also has this risk of making those inequalities worse

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if we're not super careful.

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Yeah, if we don't design and implement these systems

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responsibly.

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Exactly.

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OK, so this is definitely a complex issue.

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It is.

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So where do we even begin with all of this?

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The document mentions these four key principles, equity

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and inclusion, accountability and explainability,

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human centricity, and then global collaboration.

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Yeah, those are important.

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So how do all of these principles actually

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play into this whole justice without borders idea?

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Well, I think those principles are absolutely

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central to this whole discussion,

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like let's take equity and inclusion as an example.

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A justice system can't really be just

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if it doesn't actually serve everyone equally, right,

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regardless of their background or their circumstances.

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Yeah, for sure.

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And that's where AI can potentially

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make a big difference.

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Just imagine AI-powered tools that

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can provide legal assistance in hundreds of languages,

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making legal services accessible to communities

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that have been historically marginalized

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or just totally excluded.

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That would be incredible.

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Right.

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It's like taking those justice without borders aspirations

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and making them a reality.

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Exactly.

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But isn't there a risk that these AI systems could actually

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perpetuate existing biases?

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Oh, for sure.

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I've heard some concerns about algorithms

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being used in criminal sentencing that end up

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discriminating against certain groups.

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Yeah, you're right to raise that concern.

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OK.

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That's exactly where the principle of accountability

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and explainability comes into play.

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OK.

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If we're going to be using AI in these high-stakes situations,

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we need to be able to actually understand

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how they are making decisions.

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So we need to be able to hold them accountable.

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Yes, exactly.

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We can't just blindly trust these algorithms.

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We need what's called glass box AI.

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OK, because.

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Where that decision-making process is totally transparent

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and open to scrutiny.

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So instead of relying on this mysterious black box,

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we need to be able to look under the hood

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and see how the engine is actually working.

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Exactly.

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But how do we actually achieve that level of transparency?

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Well, there are different techniques

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you can use to make AI more explainable.

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Like, for example, instead of relying

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on these deep learning models, which can be really opaque,

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we can use rule-based systems or decision trees, where

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the logic behind each decision is super clearly laid out.

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Right, so it's not just about building systems that work.

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It's about building systems that we can understand.

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And trust.

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Yeah, and trust.

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That's essential.

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Especially when we're talking about something as fundamental

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as justice.

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Absolutely.

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So we're not just talking about handing over the gavel

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to a robot judge.

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No, not at all.

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The goal here is to use AI to augment human judgment,

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to give judges and lawyers more information, better insights,

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so that they can make fairer and more informed decisions.

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I can see how that would be really valuable,

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especially in complex cases, where there's just so much data

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to go through.

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Definitely.

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But what about the potential for bias?

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Even if humans are still making the final call,

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couldn't those biased algorithms still influence their judgment?

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Yeah, that's a totally valid concern.

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And it really just highlights the need

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for that careful design and testing of these AI systems.

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We need to be constantly evaluating,

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auditing these algorithms.

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To ensure that they're not perpetuating or amplifying

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those existing biases.

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Exactly.

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And that brings us back to that principle of equity

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and inclusion.

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It's not just about making sure the algorithms are unbiased,

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but that they are actually being used in a way that promotes

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fairness and justice for everyone.

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100%.

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And that's precisely where the principle of global collaboration

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comes in.

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This isn't just a challenge for one country or one region

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to solve.

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We need to be working together on a global scale.

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To develop ethical guidelines and standards

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for how we use AI in these justice systems.

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I'm really starting to get a sense of just how interconnected

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all of these principles are.

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Yeah, they all play together.

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It's not enough to just focus on one or two.

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We need to be thinking about all of them simultaneously.

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Yeah, if we want to create a truly just and equitable system.

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Exactly.

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So we've talked about this whole idea of AI as a double-edged

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sword.

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And we've explored those four key principles that

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can kind of help us navigate this complex landscape.

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But how do these principles actually

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play out in real world situations?

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Well, let's look at some examples.

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Yeah, let's dive into some concrete examples

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of how AI is being used in justice systems today, both

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for good and maybe not so good.

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Sounds good.

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One area where AI is already making a big impact

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is in criminal sentencing.

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Oh, yeah.

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You've got algorithm-like compass that

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are being used to try to predict the likelihood of someone

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reoffending, which judges can then consider

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when making sentencing decisions.

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But there's been a ton of debate about whether these tools

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are actually fair and unbiased.

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Yeah, compass is a really, really interesting example

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of how AI can both promise this greater fairness,

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but also raise some serious concerns about bias.

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On the surface, using an algorithm to assess risk

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seems like it could make the system more objective.

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Because human judges, I mean, we know,

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they can be susceptible to all sorts of biases,

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conscious and unconscious.

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But here's the problem.

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These algorithms are often trained

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on data that reflects the existing biases

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in the criminal justice system.

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So even if the algorithm itself is neutral,

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the data it's fed can kind of bake in those biases,

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leading to unfair outcomes.

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Exactly.

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So it's not enough to just create

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like a technically sophisticated algorithm.

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You also have to be incredibly careful about the data

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that's used to train it.

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You got it.

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And even then, predicting recidivism,

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that's inherently difficult, right?

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It really is.

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There are so many factors that contribute

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to whether someone will reoffend,

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a lot of which are impossible to really capture in a data set.

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Yeah.

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And to rely on those predictions for something

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as consequential as sentencing, I mean,

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it raises some really profound ethical questions.

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It makes you wonder if we're maybe putting a little too

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much faith in these algorithms, especially given

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that potential for error and bias.

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Definitely.

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That's why this human in the Luke model is so important.

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It is.

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These cools can provide valuable insight.

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They can.

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But in the end, the final decision

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really should rest with a human judge

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who can consider all of those nuances of the case.

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You got it.

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Speaking of nuances, let's maybe shift gears a little bit.

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And talk about access to justice.

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Huge issue globally, right?

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It is.

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Millions of people just lack access

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to even basic legal services.

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Yeah, that's right.

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So can AI actually help bridge that gap?

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Absolutely.

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There are already these AI-powered platforms

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that are making legal information and assistance

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more accessible, especially for underserved communities.

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Just imagine chatbots that can answer

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those basic legal questions, guide people

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through legal processes, maybe even help them draft

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simple legal documents.

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Wow.

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These tools can really empower individuals

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to navigate the legal system with more confidence

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and understanding.

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It's like having a virtual lawyer in your pocket.

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Right.

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That could be huge for people who can't afford

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traditional legal representation.

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Absolutely.

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And it's not just about individuals either.

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AI can also help level the playing field

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for small businesses.

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Imagine a contract analysis tool that

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can help a small business owner actually understand

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the terms of a complex contract without having

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to hire expensive lawyers.

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That kind of access to legal expertise

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can make all the difference for a struggling entrepreneur.

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It seems like AI has this potential to democratize

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access to justice in a way that just wasn't possible before.

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I think so.

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But are there downsides to these tools?

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Well, one challenge is just making sure

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that these tools are designed in a way that's truly

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accessible to everyone.

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For example, if a legal chatbot is only

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available in English, it's not going

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to be very helpful to someone who speaks Spanish.

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Yeah, or Mandarin.

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Exactly.

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We need to be prioritizing those AI tools that

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are multilingual and culturally sensitive.

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It's like we were saying earlier about equity and inclusion.

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You can have the most advanced AI in the world.

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Doesn't matter.

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But if it's not actually accessible to everyone?

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Right.

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It's not really serving its purpose.

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Exactly.

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And it's not just about language either.

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We also need to consider the digital divide.

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These tools are only effective if people actually

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have access to the technology.

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And the internet.

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Yeah, the internet connectivity.

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They need to actually use them.

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So it's not just about building the tools.

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It's about creating a whole ecosystem.

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A whole ecosystem.

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Where everyone can benefit.

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Exactly.

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00:09:34,200 --> 00:09:36,480
And that's where that principle of global collaboration

279
00:09:36,480 --> 00:09:37,480
comes in again.

280
00:09:37,480 --> 00:09:39,720
We need these international organizations,

281
00:09:39,720 --> 00:09:41,280
governments, tech companies.

282
00:09:41,280 --> 00:09:42,160
All working together.

283
00:09:42,160 --> 00:09:43,960
All working together to make sure

284
00:09:43,960 --> 00:09:46,960
that these AI-powered justice tools are

285
00:09:46,960 --> 00:09:49,880
available and accessible to everyone.

286
00:09:49,880 --> 00:09:50,440
To everyone.

287
00:09:50,440 --> 00:09:51,920
Regardless of location.

288
00:09:51,920 --> 00:09:53,160
Or socioeconomic steps.

289
00:09:53,160 --> 00:09:53,660
Right.

290
00:09:53,660 --> 00:09:55,760
So achieving justice without borders

291
00:09:55,760 --> 00:09:58,360
requires this multi-pronged approach.

292
00:09:58,360 --> 00:09:59,480
A multi-pronged approach.

293
00:09:59,480 --> 00:10:01,440
Addressing both the technical challenges

294
00:10:01,440 --> 00:10:03,000
of developing the AI itself.

295
00:10:03,000 --> 00:10:03,480
Right.

296
00:10:03,480 --> 00:10:06,800
And then also the social and economic barriers

297
00:10:06,800 --> 00:10:09,040
that prevent people from accessing justice.

298
00:10:09,040 --> 00:10:09,640
Absolutely.

299
00:10:09,640 --> 00:10:12,560
But even if we overcome all of those hurdles,

300
00:10:12,560 --> 00:10:14,480
there are still some fundamental challenges

301
00:10:14,480 --> 00:10:15,720
that we really need to grapple with.

302
00:10:15,720 --> 00:10:16,720
OK, let's talk about that.

303
00:10:16,720 --> 00:10:19,240
The document highlights three key challenges.

304
00:10:19,240 --> 00:10:19,740
OK.

305
00:10:19,740 --> 00:10:21,480
Resource gaps across countries.

306
00:10:21,480 --> 00:10:22,040
Uh-huh.

307
00:10:22,040 --> 00:10:25,920
The lack of universally agreed upon ethical frameworks.

308
00:10:25,920 --> 00:10:26,640
Right.

309
00:10:26,640 --> 00:10:29,840
And then the difficulty of establishing accountability

310
00:10:29,840 --> 00:10:31,360
for these AI systems.

311
00:10:31,360 --> 00:10:32,880
Yeah, those are some complex issues.

312
00:10:32,880 --> 00:10:33,840
They are complex.

313
00:10:33,840 --> 00:10:36,720
That are going to require some really thoughtful solutions.

314
00:10:36,720 --> 00:10:37,920
Very thoughtful.

315
00:10:37,920 --> 00:10:39,280
So let's break those down one by one.

316
00:10:39,280 --> 00:10:39,780
OK.

317
00:10:39,780 --> 00:10:42,760
What do you mean by resource gaps across countries?

318
00:10:42,760 --> 00:10:45,520
Well, developing and deploying these AI systems,

319
00:10:45,520 --> 00:10:50,080
it requires significant resources, expertise, data,

320
00:10:50,080 --> 00:10:52,240
computing power, infrastructure.

321
00:10:52,240 --> 00:10:55,640
These resources are not evenly distributed across the globe.

322
00:10:55,640 --> 00:10:56,160
OK.

323
00:10:56,160 --> 00:10:58,440
You know, a wealthy nation might have the capacity

324
00:10:58,440 --> 00:11:01,960
to create this sophisticated AI-powered justice system.

325
00:11:01,960 --> 00:11:02,460
Right.

326
00:11:02,460 --> 00:11:04,440
But a developing nation might struggle

327
00:11:04,440 --> 00:11:08,040
to even just get basic internet access to its citizens.

328
00:11:08,040 --> 00:11:09,960
So it's not a one-size-fits-all approach

329
00:11:09,960 --> 00:11:11,200
to AI for social good?

330
00:11:11,200 --> 00:11:12,120
Definitely not.

331
00:11:12,120 --> 00:11:12,620
OK.

332
00:11:12,620 --> 00:11:16,000
We need solutions that are tailored to the specific needs

333
00:11:16,000 --> 00:11:18,840
and contexts of different countries and communities.

334
00:11:18,840 --> 00:11:21,760
A justice AI system that works well in the United States,

335
00:11:21,760 --> 00:11:25,440
you know, might be completely inappropriate for a country

336
00:11:25,440 --> 00:11:28,720
with a very different legal system or cultural norms.

337
00:11:28,720 --> 00:11:31,120
It's like trying to export a legal system from one country

338
00:11:31,120 --> 00:11:31,600
to another.

339
00:11:31,600 --> 00:11:32,280
Exactly.

340
00:11:32,280 --> 00:11:33,640
It just doesn't translate well.

341
00:11:33,640 --> 00:11:34,520
It doesn't translate well.

342
00:11:34,520 --> 00:11:36,680
And that leads us right into that second challenge.

343
00:11:36,680 --> 00:11:37,180
OK.

344
00:11:37,180 --> 00:11:41,480
This lack of universally agreed upon ethical frameworks

345
00:11:41,480 --> 00:11:45,320
justice is this really complex concept

346
00:11:45,320 --> 00:11:48,960
that's shaped by culture, history, values,

347
00:11:48,960 --> 00:11:51,600
what's considered fair and just in one society

348
00:11:51,600 --> 00:11:54,640
might be seen as totally unacceptable in another.

349
00:11:54,640 --> 00:11:57,400
So how do we create ethical guidelines for AI

350
00:11:57,400 --> 00:11:58,760
and justice systems?

351
00:11:58,760 --> 00:12:00,280
That's the big question.

352
00:12:00,280 --> 00:12:04,240
When there's so much diversity in how different cultures define

353
00:12:04,240 --> 00:12:04,800
justice.

354
00:12:04,800 --> 00:12:05,840
It's a great question.

355
00:12:05,840 --> 00:12:08,120
It's going to require a lot of dialogue

356
00:12:08,120 --> 00:12:12,280
and collaboration between experts from different disciplines

357
00:12:12,280 --> 00:12:13,720
and cultural backgrounds.

358
00:12:13,720 --> 00:12:15,920
It won't be easy to find that common ground.

359
00:12:15,920 --> 00:12:16,480
For sure.

360
00:12:16,480 --> 00:12:18,680
But it's absolutely essential if we

361
00:12:18,680 --> 00:12:22,360
want to create AI systems that are truly ethical and just

362
00:12:22,360 --> 00:12:23,800
on a global scale.

363
00:12:23,800 --> 00:12:26,520
I can see how that would be a very delicate process navigating

364
00:12:26,520 --> 00:12:28,520
all those cultural differences.

365
00:12:28,520 --> 00:12:31,760
But let's say we do actually manage to agree

366
00:12:31,760 --> 00:12:34,240
on some basic ethical principles.

367
00:12:34,240 --> 00:12:36,600
How do we ensure that AI systems are actually

368
00:12:36,600 --> 00:12:38,400
adhering to those principles?

369
00:12:38,400 --> 00:12:40,840
That brings us to that third challenge you mentioned.

370
00:12:40,840 --> 00:12:42,920
The difficulty of establishing accountability.

371
00:12:42,920 --> 00:12:44,680
Yeah, this is where things get tricky.

372
00:12:44,680 --> 00:12:46,760
AI systems are complex.

373
00:12:46,760 --> 00:12:48,680
And they're decision making processes.

374
00:12:48,680 --> 00:12:49,880
They can be opaque.

375
00:12:49,880 --> 00:12:51,760
It can be difficult to determine why

376
00:12:51,760 --> 00:12:54,880
an AI system made a particular decision.

377
00:12:54,880 --> 00:12:57,600
And even harder to actually assign blame

378
00:12:57,600 --> 00:12:58,920
when something goes wrong.

379
00:12:58,920 --> 00:13:01,200
It's like trying to hold a shadow accountable.

380
00:13:01,200 --> 00:13:02,640
How do you begin to address that?

381
00:13:02,640 --> 00:13:06,920
Well, one approach is to really focus on transparency.

382
00:13:06,920 --> 00:13:09,680
The more we understand about how these systems work,

383
00:13:09,680 --> 00:13:12,840
the better equipped we'll be to identify potential biases

384
00:13:12,840 --> 00:13:14,160
or errors.

385
00:13:14,160 --> 00:13:15,920
And we need to develop mechanisms

386
00:13:15,920 --> 00:13:19,560
for auditing these systems, both during their development

387
00:13:19,560 --> 00:13:21,000
and after they've been deployed.

388
00:13:21,000 --> 00:13:22,680
So it's about building in safeguards

389
00:13:22,680 --> 00:13:24,200
from the very beginning.

390
00:13:24,200 --> 00:13:26,520
And then having that ongoing oversight

391
00:13:26,520 --> 00:13:28,320
to make sure things are actually working the way they're

392
00:13:28,320 --> 00:13:28,840
supposed to.

393
00:13:28,840 --> 00:13:29,560
Exactly.

394
00:13:29,560 --> 00:13:31,680
And when things do go wrong, we need

395
00:13:31,680 --> 00:13:34,160
these clear lines of accountability.

396
00:13:34,160 --> 00:13:38,000
Who is responsible when an AI system makes a mistake?

397
00:13:38,000 --> 00:13:39,440
That harms someone.

398
00:13:39,440 --> 00:13:42,200
Is it the developer who created the algorithm,

399
00:13:42,200 --> 00:13:45,040
the company that deployed it, the government agency that's

400
00:13:45,040 --> 00:13:45,880
actually using it?

401
00:13:45,880 --> 00:13:46,840
Those are tough questions.

402
00:13:46,840 --> 00:13:47,840
They're tough questions.

403
00:13:47,840 --> 00:13:49,640
It's like a legal and ethical maze.

404
00:13:49,640 --> 00:13:50,480
It is a maze.

405
00:13:50,480 --> 00:13:52,680
And there aren't easy answers.

406
00:13:52,680 --> 00:13:54,120
But these are conversations that we

407
00:13:54,120 --> 00:13:57,960
need to be having right now as AI just becomes more prevalent

408
00:13:57,960 --> 00:13:59,360
in our justice system.

409
00:13:59,360 --> 00:14:00,760
We can't wait until it's too late.

410
00:14:00,760 --> 00:14:01,520
We can't wait.

411
00:14:01,520 --> 00:14:04,760
We need to be thinking about these issues proactively,

412
00:14:04,760 --> 00:14:07,120
rather than waiting for something to go wrong.

413
00:14:07,120 --> 00:14:07,440
Right.

414
00:14:07,440 --> 00:14:09,000
And then trying to scramble for solution.

415
00:14:09,000 --> 00:14:09,680
Exactly.

416
00:14:09,680 --> 00:14:11,320
It sounds like there's a lot of work to be done.

417
00:14:11,320 --> 00:14:12,160
A lot of work.

418
00:14:12,160 --> 00:14:15,480
Both in terms of refining the technology itself

419
00:14:15,480 --> 00:14:18,520
and in developing the ethical and legal frameworks

420
00:14:18,520 --> 00:14:20,360
to guide its use.

421
00:14:20,360 --> 00:14:22,320
But despite all of these challenges,

422
00:14:22,320 --> 00:14:25,000
it seems like there's still this sense of optimism

423
00:14:25,000 --> 00:14:26,640
in the document, right?

424
00:14:26,640 --> 00:14:28,760
This belief that AI can ultimately

425
00:14:28,760 --> 00:14:31,200
help create a more just world.

426
00:14:31,200 --> 00:14:33,840
I think that optimism comes from recognizing

427
00:14:33,840 --> 00:14:38,600
that AI has this potential to address some of the most

428
00:14:38,600 --> 00:14:42,600
like deeply ingrained problems in our justice systems.

429
00:14:42,600 --> 00:14:45,320
Problems that have been around for generations.

430
00:14:45,320 --> 00:14:48,480
We have a real chance here to create a system that

431
00:14:48,480 --> 00:14:51,240
is truly fair and equitable.

432
00:14:51,240 --> 00:14:53,320
A system that upholds the rights and dignity

433
00:14:53,320 --> 00:14:54,320
of all individuals.

434
00:14:54,320 --> 00:14:55,160
Yes, exactly.

435
00:14:55,160 --> 00:14:57,440
It's like we're standing at a crossroads

436
00:14:57,440 --> 00:15:01,120
with the opportunity to choose a path toward a brighter future.

437
00:15:01,120 --> 00:15:02,400
We have a choice to make.

438
00:15:02,400 --> 00:15:04,520
That path won't be easy, though.

439
00:15:04,520 --> 00:15:09,160
It's going to require vision, courage, and a willingness

440
00:15:09,160 --> 00:15:11,200
to challenge that status quo.

441
00:15:11,200 --> 00:15:11,960
We have to be bold.

442
00:15:11,960 --> 00:15:14,520
We have to be bold in our aspirations,

443
00:15:14,520 --> 00:15:17,960
while also remaining rounded in the realities of what's

444
00:15:17,960 --> 00:15:18,960
possible.

445
00:15:18,960 --> 00:15:22,400
And we need to be constantly questioning, testing our

446
00:15:22,400 --> 00:15:25,280
And always striving to do better.

447
00:15:25,280 --> 00:15:28,080
So it's not just about developing the technology.

448
00:15:28,080 --> 00:15:32,600
It's also about having the wisdom to use it responsibly.

449
00:15:32,600 --> 00:15:36,400
And ethically, and what wisdom comes from understanding

450
00:15:36,400 --> 00:15:38,920
the complexities of the human experience,

451
00:15:38,920 --> 00:15:43,120
the nuances of justice, and the potential consequences

452
00:15:43,120 --> 00:15:44,040
of our actions.

453
00:15:44,040 --> 00:15:44,540
Right.

454
00:15:44,540 --> 00:15:46,800
We need to approach AI with a sense of humility.

455
00:15:46,800 --> 00:15:47,520
Humility.

456
00:15:47,520 --> 00:15:50,640
Recognizing that it's this powerful tool that

457
00:15:50,640 --> 00:15:52,800
can be used for good or for bad.

458
00:15:52,800 --> 00:15:54,960
And it's like we said at the beginning, right?

459
00:15:54,960 --> 00:15:56,440
AI is a double-edged sword.

460
00:15:56,440 --> 00:15:57,120
It is.

461
00:15:57,120 --> 00:16:00,440
And it's really up to us to decide how we wield it.

462
00:16:00,440 --> 00:16:01,200
That's right.

463
00:16:01,200 --> 00:16:01,600
Wow.

464
00:16:01,600 --> 00:16:03,320
We've covered a lot of ground in this deep dive.

465
00:16:03,320 --> 00:16:03,960
We have.

466
00:16:03,960 --> 00:16:07,960
From the potential of AI to totally revolutionize access

467
00:16:07,960 --> 00:16:11,880
to justice, to those very real risks of bias.

468
00:16:11,880 --> 00:16:15,400
And those big challenges of creating a global framework

469
00:16:15,400 --> 00:16:17,040
for ethical AI development.

470
00:16:17,040 --> 00:16:17,540
Yeah.

471
00:16:17,540 --> 00:16:18,800
It's a lot to think about.

472
00:16:18,800 --> 00:16:22,040
It's clear that this technology is rapidly evolving.

473
00:16:22,040 --> 00:16:22,600
It is.

474
00:16:22,600 --> 00:16:24,960
And its impact on our legal systems,

475
00:16:24,960 --> 00:16:27,520
it's only going to grow in the coming years.

476
00:16:27,520 --> 00:16:30,960
It's an exciting time to be thinking about these issues.

477
00:16:30,960 --> 00:16:34,920
We're at this pivotal moment where the choices we make today

478
00:16:34,920 --> 00:16:37,320
are really going to shape the future of justice.

479
00:16:37,320 --> 00:16:38,840
Yeah, for generations to come.

480
00:16:38,840 --> 00:16:41,680
And that brings me to something I've been kind of pondering

481
00:16:41,680 --> 00:16:43,160
throughout our conversation.

482
00:16:43,160 --> 00:16:47,880
What role do individuals, like our listener, play in all of this?

483
00:16:47,880 --> 00:16:50,880
It's easy to feel like these are massive complex issues that

484
00:16:50,880 --> 00:16:52,960
are just totally beyond our control.

485
00:16:52,960 --> 00:16:55,560
But as we've been discussing AI development,

486
00:16:55,560 --> 00:16:57,640
it's not happening in some vacuum.

487
00:16:57,640 --> 00:16:58,160
It's not.

488
00:16:58,160 --> 00:17:00,760
It's shaped by the values.

489
00:17:00,760 --> 00:17:02,760
And the priorities of the people who

490
00:17:02,760 --> 00:17:04,200
create and deploy these systems.

491
00:17:04,200 --> 00:17:04,920
Absolutely.

492
00:17:04,920 --> 00:17:08,200
Which means that we, as individuals,

493
00:17:08,200 --> 00:17:10,880
we have a responsibility to engage with these issues.

494
00:17:10,880 --> 00:17:11,480
We do.

495
00:17:11,480 --> 00:17:12,800
To ask those tough questions.

496
00:17:12,800 --> 00:17:13,300
Yeah.

497
00:17:13,300 --> 00:17:18,000
And to demand that AI is used in a way that benefits all

498
00:17:18,000 --> 00:17:18,800
of humanity.

499
00:17:18,800 --> 00:17:19,480
100%.

500
00:17:19,480 --> 00:17:21,800
We can't just passively accept whatever

501
00:17:21,800 --> 00:17:24,520
technological solutions are presented to us.

502
00:17:24,520 --> 00:17:26,360
We need to be informed, critical thinker.

503
00:17:26,360 --> 00:17:29,480
We need to be able to discern those potential benefits

504
00:17:29,480 --> 00:17:30,800
and risks of AI.

505
00:17:30,800 --> 00:17:33,920
And we need to be vocal advocates for the kind of future

506
00:17:33,920 --> 00:17:35,000
that we want to see.

507
00:17:35,000 --> 00:17:36,840
So what can our listener actually do?

508
00:17:36,840 --> 00:17:37,640
Yeah, what can they do?

509
00:17:37,640 --> 00:17:39,360
Practically speaking, to actually make

510
00:17:39,360 --> 00:17:40,840
a difference in this space.

511
00:17:40,840 --> 00:17:42,600
Right, because it can feel daunting.

512
00:17:42,600 --> 00:17:43,100
It can.

513
00:17:43,100 --> 00:17:44,240
And know where to even begin.

514
00:17:44,240 --> 00:17:44,740
Yeah.

515
00:17:44,740 --> 00:17:48,200
A great first step is honestly just to stay informed.

516
00:17:48,200 --> 00:17:48,700
It's OK.

517
00:17:48,700 --> 00:17:49,640
Read articles.

518
00:17:49,640 --> 00:17:51,800
Listen to podcasts like this one.

519
00:17:51,800 --> 00:17:52,300
Right.

520
00:17:52,300 --> 00:17:54,160
Engage in those conversations about AI

521
00:17:54,160 --> 00:17:55,680
and its impact on society.

522
00:17:55,680 --> 00:17:56,180
Right.

523
00:17:56,180 --> 00:17:58,000
The more you understand about these issues,

524
00:17:58,000 --> 00:17:59,480
the better equipped you'll be.

525
00:17:59,480 --> 00:17:59,980
Yeah.

526
00:17:59,980 --> 00:18:01,400
To participate in the debate.

527
00:18:01,400 --> 00:18:03,440
And to make informed choices.

528
00:18:03,440 --> 00:18:05,480
And even of informed choices, you

529
00:18:05,480 --> 00:18:07,480
mentioned earlier some resources that we'd share.

530
00:18:07,480 --> 00:18:08,160
Oh, yeah.

531
00:18:08,160 --> 00:18:09,400
Those are a great place to start.

532
00:18:09,400 --> 00:18:09,920
Definitely.

533
00:18:09,920 --> 00:18:12,240
We'll be sure to include links to some valuable resources

534
00:18:12,240 --> 00:18:13,760
in the show notes for this episode.

535
00:18:13,760 --> 00:18:14,400
Perfect.

536
00:18:14,400 --> 00:18:17,240
But it's not just about acquiring knowledge.

537
00:18:17,240 --> 00:18:17,600
Right.

538
00:18:17,600 --> 00:18:20,440
It's also about actually putting that knowledge into action.

539
00:18:20,440 --> 00:18:21,040
Exactly.

540
00:18:21,040 --> 00:18:23,080
There are so many ways to get involved.

541
00:18:23,080 --> 00:18:25,600
You can support organizations that

542
00:18:25,600 --> 00:18:28,040
are working to promote ethical AI development.

543
00:18:28,040 --> 00:18:28,440
Right.

544
00:18:28,440 --> 00:18:30,760
You can contact your elected officials.

545
00:18:30,760 --> 00:18:31,040
Right.

546
00:18:31,040 --> 00:18:33,160
Urge them to prioritize these issues.

547
00:18:33,160 --> 00:18:33,600
Yeah.

548
00:18:33,600 --> 00:18:36,200
Even something as simple as just having conversations

549
00:18:36,200 --> 00:18:39,760
with your friends and family about AI and its implications

550
00:18:39,760 --> 00:18:40,880
can help raise awareness.

551
00:18:40,880 --> 00:18:41,380
Yeah.

552
00:18:41,380 --> 00:18:42,840
And to mark that critical thinking.

553
00:18:42,840 --> 00:18:45,800
Those everyday conversations can be super powerful.

554
00:18:45,800 --> 00:18:46,280
They can.

555
00:18:46,280 --> 00:18:48,040
They challenge assumptions.

556
00:18:48,040 --> 00:18:49,320
They shift perspectives.

557
00:18:49,320 --> 00:18:49,680
Yeah.

558
00:18:49,680 --> 00:18:51,280
And they inspire people to take action.

559
00:18:51,280 --> 00:18:52,120
That's right.

560
00:18:52,120 --> 00:18:54,480
It's this reminder that we're not just

561
00:18:54,480 --> 00:18:57,000
like these passive consumers of technology.

562
00:18:57,000 --> 00:18:57,600
Right.

563
00:18:57,600 --> 00:18:58,680
We have a voice.

564
00:18:58,680 --> 00:18:59,200
We do.

565
00:18:59,200 --> 00:19:01,160
And we can actually use it to shape the future.

566
00:19:01,160 --> 00:19:04,000
100% and don't underestimate the power

567
00:19:04,000 --> 00:19:05,760
of your individual choices.

568
00:19:05,760 --> 00:19:06,200
OK.

569
00:19:06,200 --> 00:19:10,800
As consumers, we have a say in which companies we support

570
00:19:10,800 --> 00:19:13,760
and which products we use, we can choose

571
00:19:13,760 --> 00:19:15,560
to invest in those companies that

572
00:19:15,560 --> 00:19:18,520
are committed to ethical AI development.

573
00:19:18,520 --> 00:19:18,880
Yeah.

574
00:19:18,880 --> 00:19:22,560
And we can demand that transparency and accountability

575
00:19:22,560 --> 00:19:24,240
from the companies we do business with.

576
00:19:24,240 --> 00:19:27,280
It's a reminder that even small actions

577
00:19:27,280 --> 00:19:28,440
can have a ripple effect.

578
00:19:28,440 --> 00:19:28,880
They can.

579
00:19:28,880 --> 00:19:31,840
By making those conscious choices in our everyday lives.

580
00:19:31,840 --> 00:19:32,480
Yes.

581
00:19:32,480 --> 00:19:35,680
We can send a message that we care about these issues.

582
00:19:35,680 --> 00:19:36,520
Exactly.

583
00:19:36,520 --> 00:19:37,560
And that we demand better.

584
00:19:37,560 --> 00:19:38,240
We demand better.

585
00:19:38,240 --> 00:19:40,720
From the companies that are shaping our technological future.

586
00:19:40,720 --> 00:19:41,280
That's right.

587
00:19:41,280 --> 00:19:43,480
And as we wrap up here, I want to bring us back

588
00:19:43,480 --> 00:19:46,840
to that vision of justice without borders.

589
00:19:46,840 --> 00:19:47,340
Yeah.

590
00:19:47,340 --> 00:19:49,480
It's a bold, ambitious goal.

591
00:19:49,480 --> 00:19:50,000
It is.

592
00:19:50,000 --> 00:19:52,360
But it's one that's absolutely worth striving for.

593
00:19:52,360 --> 00:19:52,800
Right.

594
00:19:52,800 --> 00:19:56,360
And while it may seem far off, I believe that by working

595
00:19:56,360 --> 00:19:59,120
together, by embracing those principles we've

596
00:19:59,120 --> 00:20:02,880
talked about today, and by demanding better for ourselves

597
00:20:02,880 --> 00:20:05,480
and from those companies and institutions that

598
00:20:05,480 --> 00:20:07,960
are wielding these powerful technologies,

599
00:20:07,960 --> 00:20:11,920
we can create a world where justice is truly accessible to all.

600
00:20:11,920 --> 00:20:14,080
It's been such a thought provoking conversation.

601
00:20:14,080 --> 00:20:14,720
It has been.

602
00:20:14,720 --> 00:20:16,760
Thank you so much for joining us on this deep dive.

603
00:20:16,760 --> 00:20:17,640
Thanks for having me.

604
00:20:17,640 --> 00:20:19,840
And to AI for social good and justice systems.

605
00:20:19,840 --> 00:20:20,560
It's been great.

606
00:20:20,560 --> 00:20:23,440
We hope you found it informative, engaging,

607
00:20:23,440 --> 00:20:25,280
and maybe even a little bit inspiring.

608
00:20:25,280 --> 00:20:25,880
I hope so.

609
00:20:25,880 --> 00:20:27,920
And remember, this conversation doesn't end here.

610
00:20:27,920 --> 00:20:28,520
It doesn't.

611
00:20:28,520 --> 00:20:30,600
Keep exploring, keep questioning,

612
00:20:30,600 --> 00:20:33,760
and keep pushing for a future where technology serves

613
00:20:33,760 --> 00:20:34,320
humanity.

614
00:20:34,320 --> 00:20:34,920
Yes.

615
00:20:34,920 --> 00:20:36,040
Not the other way around.

616
00:20:36,040 --> 00:20:36,600
Well said.

617
00:20:36,600 --> 00:20:38,560
Thank you for listening.