Implementing Responsible, Trusted AI Systems: A Fireside Chat with IBM

All right, everyone, welcome. This is uh implementing responsible trusted AI systems of Fireside Chat. Uh I'm Chris uh here with Lauren Frazier from IBM. Today we want to talk about exactly what says in the box building trusted artificial intelligence. Um before we begin, it's just a bit of housekeeping.

Uh, wherever it is you're tuning in, please go ahead and you know leave a comment, let us know where you're in from. If you are on, if you're watching us on Facebook, you will need to go to StreamYard.com/slash Facebook if you want us to know your names. If you just want to be, you know, anonymous, cheetah or whatever in the comments, that is fine as well. But if you're on uh Facebook, go ahead and leave your comments there uh after authenticating. So uh Lauren, why don't you introduce yourself real quick?

Yeah, thanks for hosting us today, Chris. Um, I'm Lauren Frasier. I'm an IBM content marketing manager with IBM Cloud Pack for Data. So that's our leading data in AI platform, runs on any cloud, and hey, we're focused really on trustworthy AI right now. So the timing couldn't be any better.

So we can go ahead and kick it off and you know, discuss the responsible AI, especially, you know, now the stakes are higher, right? AI can be used for good, or if you use it wrong, it'll have negative consequences, whether that means in money, financials, or just trust with your customers. So businesses that handle data, they can no longer just ignore the societal responsibilities. We really need to put that at the forefront of operationalizing AI. How do we make it trustworthy?

So, Chris, my first question for you is why is it important and what implications are there in deploying AI and while all especially ensuring the responsible AI is um infused within. You know, it comes down to if we want to trust something, um, we need to know that it's going to operate, you know, with fairness and stuff. There's there's a lot that goes into trust, but fundamentally, we're trying to roll out this technology as a society, as a civilization, to as many all these different applications, right? Mortgage and loan applications, uh, criminal recidivism, uh, you know, more mundane stuff like marketing effectiveness, which is sort of the area that I study. And we need to know that the machines are doing what we want them to do and not exposing us to unnecessary risk.

You know, um, there are no shortage of examples where AI hasn't been used responsibly, right? It hasn't been built to be trustworthy. And I think that we should probably like define what trustworthy means. Uh, if you go to um research.ibm.com, there's actually a really good whole section on trusted AI, but there's four fundamental things that make AI trustworthy. Fair, is it accountable?

Is it values driven? And then is it explainable? Uh real quick, Lauren, when you think about fairness, what is what does that word mean to you? For fairness for me, it means equality. It means you know, people are being treated all the same, no matter what.

That data is used fairly. So that means data is used properly. It's used for the good of people, the good of the world, the good of making decisions and better business decisions, which ultimately brings in the money but also changes and impacts the world. And it doesn't matter who and what that person does, but fairness is giving everybody that equal slate. Yeah, it's it it's challenging because there's different definitions of fairness, right?

Um, you know, some real simple examples. There's there's this what's called blinded fairness, where you say anything that is protected, your age, your race, your gender, that data is removed. It can't be used for decision making. It's kind of like the the bare bones. But one of the things that AI is really good at is doing what's called correlates, where you say, okay, I may not know your age, but if you like, you know, Goonies and you like uh, you know, uh I'm trying to try to go way back, like MC Hammer in the early days and stuff.

We can infer your age, right? Because you the these things that you like all have a certain timeliness to them. So that's one aspect. A second would be what's called representative parity, where if I'm trying to sample some data, I try to make the data represent the population. I used to work at a company in Atlanta, and on staff at a hundred person company, there wasn't a single black person.

Yeah. Atlanta's 54% black. And and pretty good community, yeah. Exactly. Uh so there was a that's a case where there is not representative parity.

Um then there's the two where we can have real, you know, significant philosophical debates. Uh, equality of opportunity and equality of outcome. Equality of opportunity means we all get the same chance at success, but success is left up to our individual merits. And then equality of outcome is no matter who we are, we all get the same thing. And there are definitely cases where, like COVID vaccines, we want equality of outcome.

Everybody gets it. Right. Um, everybody gets it, but you know, how hard it was to get it, AI could have been used more to drive who needs to get that first, instead of us, for instance, me fighting over a Ventbrite in my mom also in a whole other state trying to get my nana who's nine to get back scene. AI could have helped us just improve that. And hopefully we don't have to see that going forward, but it we will be ready if something, you know, a health crisis does come up again.

Exactly. So fairness is part one of trusted AI. Two is accountability, where the machine tells us how it made its decisions. So I go to apply for a loan, and it says, hey, Chris, your loan was denied for you know because your credit score was below 670 or your household income was insufficient. But it should also tell us what wasn't involved in the decision.

Like, hey, Chris, the fact that you're a guy wasn't a factor in the decision. The fact that you're old wasn't a factor in your decision. Um and we need our machines to tell us like this is how I made the decisions. And a lot again, a lot of machines, they're very, very opaque. They they won't tell us what's going on.

Um our AI should be values driven. And this is where I'm just gonna get myself into a whole bunch of trouble here. Um our companies and the values that we have as human beings inform the data that we put into these machines. AI is nothing more than a bunch of math, right? It's not magic, it's math, and it's math that's trained on data.

So the data we put in means that that's what the machine learns to write its own code from. We have to have values that are aligned with the outcomes we want. There's a oh what the heck, you know, if you look at some of the things that like Facebook does, and they have been rightly criticized in the public press um for making some questionable decisions. And if you look at their core values, be bold, focus on impact, move fast, be open, build social value. At no point in there does it say make the world a better place, make people healthier, promote truth.

You know, these are other values that other companies might have. And so their AI reflects their values. So as part of trusted AI, you almost have to trust the company making the AI. Yeah. And essentially, as a customer, we don't, as a cut consumer of Facebook or of anything, or even just applying for an HR loan, or or you know, even behind the scenes in AHR applying for a mortgage loan, you don't understand the big technology around it.

So, you know, companies have to make sure that there's a way that they can explain it because I think you know, you don't want to be in the court of law, you don't want to be the front page on the news, and then that's when your customer realizes, oh wait, my data hasn't been being used properly. So, I mean, I guess with the rise of all of these events last year, too, including how we work in the pandemic and some of these societal and political events I think we all know of. Um, I think data reflects the drastic changes in human behaviors itself. So, as we kind of talked about already, the pandemic, but what else do you see that is different from last from this year from last? And why does this matter today and in the scope of AI?

Um, I want to go back real quick. There's one more piece of trusted AI that I think matters that and it answers this question really well. Is that is the difference between explainability and interpretability? AI has to be both of these things. Um, the analogy that I love to use is uh imagine you're at a famous restaurant, right?

And you and you're trying to figure out like what they use to make that chocolate cake. Just by tasting it, you can tell, like, okay, this is pretty good. Then you go home and try it yourself. Like, okay, I can't, I it's not quite the same. That's explainable AI, right?

Well, you see the outcome of the model and you go, okay, this looks okay. It seems okay. Interpretable AI is when you are in the kitchen with the chef watching them, like, oh, you're not using almond extract, you're using cherry extract. That's that's at this or oh, you you let that rise for a little bit longer than normal. Why do you do that?

Like it helped develop the flavor or something. When we're talking about interpretable versus explainable AI, a lot of companies are really pushing for explainable because it's cost effective to just say, Oh, yeah, the model did the right thing, see the results. They're like, Yeah, but I don't know that. If I am concerned about things like bias in my AI, that's not good enough. One of the things I love about tools like IBM Watson Studio is that it will, in the in the auto AI module, it'll build a model, but then you push a button says, turn this back into code, and then it turns it back into code.

Now I can step through line by line and say, what decisions did you make? How did you build this code? And if I see something like, ah, you did use something I told you not to do, I can take that out. Because you're right. In a court of law, I'm gonna need to produce the code.

Um, and I honestly think that's okay. I for humans, we have a presumption in law that you're innocent until proven guilty. I almost feel like with machines, it should be the reverse. Like the machine should be suspected of bias until we can prove that it isn't. And we prove it isn't by producing the code.

The challenge is, and the reason why so many tech companies don't want to go that route is it's expensive. It's it's cost exp cost inefficient and it's controversial. But going to your question about what's different this year than last year, the pandemic has been essentially a world war, right? It's a world war of the entirety of the human race against a very, very, very tiny enemy that can replicate like crazy. And it's a crisis.

And the funny thing about crisis is that it brings out, it amplifies things. The good gets better, the bad gets worse. And all the things, the inequalities, the inequities in our healthcare system, our income gaps, pay gaps, get worse in a crisis. Just like you see what was it, the stat, GoFundMe is the is America's third largest health insurance plan, right? People asking for help, begging for help is the third largest health insurance plan.

Now, to your point, AI could help solve a lot of these things. It was deployed responsibly and in a trustworthy way. But the challenge is things like training data that goes into it, we have to help people build better systems that say that look for bias at all times in the system. So we have to say, you know, is the data going unbiased? Is the model biased?

And does the model drift? You know, again, one of the things I like in Watson Studio is that it tells you in the model monitoring, hey, this thing's going off the rails. You want to do something about it. No, that's true. And I think it's important that we just also with Watson Studio, you are able to model or monitor that model, but also interpret and explain.

And that's the key to get things you were saying. It's not just about explaining, but proving it to anybody and also making it easy for maybe that court of law or the external folks to understand, okay, I see how my data was used. If I ever did need to ask that. So, you know, I know that for us, IBM's always strive with imminent innovation and bring in benefits to everyone and not just a few. And I think even in hiring, you know, I my my own team is pretty diverse.

So I I have enjoyed being at IBM for the past seven years, but this philosophy is also applied to AI, and we aim to create and offer reliable um and understanding technology. Um we understand that AI is embedded in everyday life, right? Which we're you were talking about, whether it's business, government, medicine, healthcare, all of that. But our goal is to help people and organizations adopt it responsibly. So I know we kind of define trustworthy AI.

Would you define responsible AI as the same thing as that? And what are the opportunities and challenges that might come with the use of ethical AI? Trust is what you build up front, responsible is what happens after, right? So you it's kind of like uh it's kind of like any relationship, right? You you build a relationship, you build trust up front, and then on the back end you prove that that trust was well founded or not, right?

Um, depending on the technology. When you look at at the ethical use of AI, it's funny, ethics is a tricky word because ethics, you know, sort of the the classical Greek sense means you do what you say, right? If you look at you know the way Facebook influences AI, they do what they say. At no point do they say they're going to make the world a better place. We're gonna set the whole world on fire.

But it's ethical because they're doing what they said. Um, the the question that we have to ask ourselves as as the people who maintain AI is are we doing the things that we want to accomplish? Are we creating the outcomes that we think are fair that and that are equitable? And for uh a more practical way of things looking at, are we doing things that are gonna get us sued? Right?

If if in a court of law are like, oh yeah, sorry about that. We accidentally um when you have those those data sources, you know, inside the machines, there's so many ways can go wrong. I was at a um I was at a conference a couple of years ago, the Martech conference, uh, which and and of course every vendor on the floor had you know we have AI on our product, like yeah, it's not it's not Nutella, guys. You don't it doesn't need to be on everything. Um this one vendor had this map of Boston, yeah, and they were trying to predict ideal customers, and it's for Dunkin' Donuts, right?

Now, if you for those of you who are, I know we see in the comments you have people listening from all around the world, Dunkin' Donuts is sort of a mass market um uh coffee and donuts shop, right? And pretty much everybody in New England, the New England region of the United States, um consumes Dunkin' Donuts in some form. Um the only people who don't are dead. Um and this company tried to predict these ideal customers, had this map of Boston. There were red dots in the areas that were I you know ideal, and then they were you know black dots in the areas that weren't.

I looked at this map and I said, So you think the ideal customers all in the financial district, downtown, Cambridge, uh Roxbury, Dorchester, and Matipan, which are predominantly lower income, predominantly black areas, say there's no ideal customers. I'm like, I'm sorry, you're full of shit. Um, because they're everybody in Boston, regardless of race, gender, background, you you consume dunks in some fashion. And I said, What you really did is you reinvented redlining. Yeah.

Which is again, yeah. So again, for those folks who are who are not familiar with American history, in the 1930s, insurance companies would take maps of the world of the cities and draw red lines around predominantly you know minority areas of the city and say we don't want to give loans in these areas. Um it that's not an equitable outcome, but particularly for something like coffee. Like, okay, if you're selling stream airplanes, then yes, there's an argument to be made that some sections of the city by income level, you might be justified that. But you're selling coffee, you're selling a dollar coffee, everybody can get that.

And so with that you know, ethical responsible use of AI, we have to think about what kind of risk are we opening ourselves up to if we implement it badly. And I think it's important to also say, I think it's something you mentioned before, is who's in the boardroom? Who's behind there making these decisions? So I think someone in the chat brought up a good question is where do you get training data when you know the data itself does not represent the overall pool accurately? You know, if put folks aren't behind the scenes and can say, you know, wait, this is redlining again.

This is because clearly someone didn't look at that, it's just quite obvious that we're just doing something that we're trying to, it's still kind of going on sometimes, and we're trying to pivot and and change the world, right? So, how do people get that correct data? How do we cleanse it? How do we even get there? Data data's too far gone at that point.

You actually raised a really good point. This you can get bias in AI, all kinds, including allowable bias, uh, to creep in at six different spots in the process. But the number one place where it starts is in the people you hire, right? Yeah. If the people you hire, and I'm not saying that you're hiring biased people, but if you're if you hire people who don't think to ask the question, hey, is there a bias in this data?

Then you will never get to the point where the systems can detect it. Now, then you get somebody to say, hey, I think this data might have a problem. I don't know what it is, but there might be a problem in here, and it's built into your strategy, which is the second place uh can creep in, then there are tools that you can use to assess your data. IBM has a great uh toolkit called the AI Fairness 360 toolkit. It's free, it's open source.

You can use it in R and Python. I use the R version, and you feed it data and it says, Hey, what are the protected classes, right? What are the things that you that cannot be discriminatory? What kind of fairness are you looking for? We talked about the different kinds of fairness, and then what do you want to do about it?

And it will say, like, yes, there's a skew of plus or minus this percentage, or there are issues here, and then it's up to us to say, how do we want to deal with that? In Watson Studio, um, you can handle this at a couple different points. In the in the data with the model building side up front, you can actually with the toolkits help flip bits. So if I have a say a day uh data set that's 60% male and 40% female, uh Watson Studio with with our guidance can say, and we you have to tell it to do this. Um, I want you to flip the bit, randomly sample the data set and flip the bit on 10% of the males to turn them female so that it it balances the data set out.

Um, the model monitoring does the same thing as well. It'll say, okay, I can flip bits around or change data around to try and remix the sample to keep it fair, to keep it on the rails. Um, the other option is you you you know filter the data up front and say, okay, I'm gonna do say propensity score matching, and I'm only gonna allow an even gender split, or I'm only gonna allow a representative population split in the data so that what goes into the training uh for the model quick construction is fair to begin with. So it's a really good question. It's a challenging question because you have to be aware of how to do these things.

Yeah. And aware of what bias is. Exactly. So I guess that goes into kind of the automation of AI. You know, more companies are used using AI operationalizing, but only by embedding ethical principles into these applications and processes can they really be built on trust, right?

So, what do you see as a creep key criteria for bringing models to production and driving value from the deployments? And what do you see in like trends in the architecture that um folks are adopting or should adopt? Yeah, uh, there's there's a few things here that I think are important. Um, one is automated machine learning has really come a long way. Oh, uh Loris was asking in the comments uh the link to the IBM tool.

If you go to AIF360.myBlueMix.net, I put a link in the comments, that's the AI Fairness 360 toolkit. Um so there's there's a few different components that you need to have in the system. And here's the challenge that again, a system like Cloud Pack for Data will address that you know, a sort of a mixed bag of individual solutions will not necessarily be do because they're not connected to each other. So you really want that integration. Um you need to be able to get at the data where it where it lives, right?

So um being able to use something like Red Hat OpenShift to to virtualize the data out of where it is and make it into a common layer. You need a system like AI Fairness 360 to look at the data and say, okay, is there bias going into it? Is that you know what kinds of issues are there? You need I like tools like um Watson Studios Auto AI because in some ways it takes some of the decision making and the potential biases I have as a as a data scientist out because it'll yeah, I have feed it a data set and say, here's the 44 things I tried. Um, here's the best result, here's the seven different measures of of accuracy, which you know I think this is the best one, but then I can always go back and uh I can push the button and say, okay, generate the code.

I can always go back and say, I really actually want to use gradient boosting for this. Um so you need you have that sort of that in the the model construction phase, then you have deployment, you've got to get that model into production, and then you have to monitor the model as well. And this needs to be an ecosystem that where the pieces talk to each other as opposed to being you know individual point solutions, because what hat tends to happen with point solutions is they break really easily. Um I can pass a model from say you know our studio into uh a standalone platform, but that standalone platform can't monitor drift and then can't pass back into my original code and say this is this is a problem. I have to do that manually.

And if I'm you know working on five or six projects for you know different clients, whatever, I may not remember to do that. If I've got a system like Cloud Pack for data in Watson Studio, it does it for me, right? So I in a lot of ways it it takes my biases out of the equation, and it also automates a lot of the the maintenance and the the operation of AI, and that that part is it's something people don't think about. When when people think about AI, they think it's like this magical unicorn that you know you just strap your data to it and it flies off into the sky. Here it goes.

No. Exactly. And it's not, it's the oh, it's almost like AI really is nothing more than really fancy spreadsheets, right? You don't expect Microsoft Excel to run itself. You have to do stuff with it.

Um, and in the same way, AI is just software, except it's software that a machine wrote from your data. So you want that ecosystem so that it's running your your data, your models, your and and monitoring all in one place. And that way it can tell you proactively, I think something's wrong here. And your whole team gets the visibility of it as well, not just you. You can see where where the issue happened, how can we go back?

What where can we, you know, mitigate that risk or mitigate that bias? And you know, I know you already brought up HR, and um, I know one of IBM's biggest clients is using AI to ensure um hiring and other HR practices are fair, and especially with corporate policies and the social responsibilities of today. But what kind of client questions are you getting when it comes to operationalizing AI or the use of AI? You know, it's funny. Um our clients in a lot of ways don't care about AI.

What they care about is better, faster, cheaper results. Right? We want things to work better. We want uh more accurate models. We want not even the the models, you know, uh one of our larger clients, an automotive client, they just want the to know what to do.

Help me make better decisions faster. Um but going into that, you know, there's a lot of challenges. The biggest challenge that a lot of people face are is you know, it might it it mirrors the AI lifecycle. Do you have the right people? Do you have the right strategy?

Do you have the right data? Do you have the right algorithm choices? Do you have the right models? And do you have the right monitoring to keep it all intact? That hierarchy, that that process in a lot of cases is really broken in a lot of companies.

They don't have the right people, which is why they need you know firms like Trust Insights and companies like IBM. They have a strategy, but the strategy may not be optimized for AI because AI is all about getting machines to do things that humans, you know, tasks that humans do. And if you're not thinking about being process-oriented, and you're not thinking about how do I be efficient, then your AI is not really going to work for you. Um, and then the big one by far is the data's a hot mess. Right?

It's everywhere. Yeah. Exactly. The data is everywhere. It's in the wrong formats, it's not structured well, it's corrupted.

Um something as simple, like one of the things we see a lot when we're doing marketing analytics is hey, you launched a new website and you forgot to put your Google Analytics tracking code on it for three weeks. So you go back in the data, there's a big three-week gap here, like what happened, guys? Like, oh, they they something as simple as that can really hose even basic analysis. Um then there's all this stuff around the humans. So, how do we communicate what AI is doing to people to the people who are the stakeholders?

How do we help them understand that you will get better outcomes? How do we show them some early easy wins? So one of the things that we do a lot of is attribution analysis. Take all the data that you have and say, these are the things that work in your marketing. That's a pretty easy win because it helps people understand.

Oh, I'm investing 44% of my budget in Facebook ads, but it's delivering 2% of my leads. They get it. Where else can we put this? Yeah. Exactly.

So those are a couple of the examples where we use it extensively. We're actually uh working on a couple other projects where we're trying to look at building you know ongoing running models that help do some predictions, some forecasting. Uh, we just did one uh recently doing predictive analytics, just helping a client understand, hey, here's what's likely to happen in the next three months for this particular type of content. Um, you should time your promotions to when interest by the audience is going to be the highest because that's when people are are paying attention. People are like they're like spotlights, right?

And you know, the spotlight moves around, you know, it's watching you know the Falcon uh the Winter Soldier. Um it's it's watching it's such a good show. Um, but you have you earn very small amounts of time with people's attention. But when they're thinking about something that they care about that is is something you solve, the spotlights on you. So what can you do to take them advantage of that time?

If you're not prepared, the spotlight hits you and then moves on, right? And you're like, well, good they go. But if you use predictive analytics, if you use AI intelligently, when the spotlight hits you, you're like, hey, here's the thing you can buy, and then they buy it, and then the spotlight moves on. Yeah. And that's the real operationalizing of AI is not just getting the system running, but getting the benefits from it.

And it's not just the benefits, it's being, or it's the benefits, but for predicting those outcomes and intelligently through automated processes, I think are key. And it's also, I think it goes back to what we were saying at the beginning. It's not just about the business impact, it's about the impact to the world you're making and to your customers and how you're improving lives by these decisions. Uh, whether that's uh loans, whether that's uh, you know, even data for uh for universities and students. So there's so many ways that data can be used.

So before Laura go ahead, we have to be very careful about to when we should not be using AI, right? And I think there are cases where it is clear that AI is the wrong choice. Uh I'll give you two examples. Okay. Example one uh ProPublica did an investigation in 2016 of police departments creating an algorithm attempting to predict recidivism.

Uh recidivism, for those who don't know, is the likelihood that someone will re-offend or commit another crime. The algorithm that this company came up with uh predicted that black Americans re would re-offend five times more than they actually did, but even worse, the algorithm itself was 20% accurate, right? You can flip a coin and do better than this algorithm by a substantial margin, not just a little bit. That was a case where it's still not clear whether somebody had their thumb on the scale and it was intentionally biased, um, or if they just fed it such bad data that it came up with it. So that's a case where the data was bad, and the the people putting it together probably didn't know what they were doing, or they did, and it was malicious.

Second example, this is a big one. AI is not a good choice in a lot of things like healthcare and finance for specific populations, not because AI, the technology is bad, right? But because the whole data set is corrupted. Example, black American healthcare outcomes. You can't, there is zero good data about it.

Zero in the entire country. Why? Because systemic racism has created such a mass of negative outcomes that it does not matter where you sample your data from, it's going to be bad. What you have to do is kind of look what we were talking about with um Watson Studio, where you have to almost change other people's races in a system to introduce known good data to say, like, yeah, the ideal health expectancy outcome should be like 78 years old. Um because of problems that are outside the data, macro systemic problems, you can't trust that data.

So one of the things that you have to ask yourself when you're deploying AI is is the data itself so corrupted that it cannot be usable, that you can't recover it. Um and there's no good data to be found. If that's the case, then AI is not the right choice. Um, you will have to rely on boring old natural intelligence at least until you have better data. And I'd rather us rely on that, but I do have hope for hope for the future that you know, hopefully, these companies, I know that IBM is striving for it, but hopefully we continue to see just from the past year and all of these items being brought to the forefront, right?

I think there's been a lot more visibility on how just much systemic racism has affected all of us and outcomes. And I just hope that all organizations start to think how can we really start to go behind the scenes, look at our data from the beginning. Is this how what we should even be using? And hopefully in the future, it could be used for good in those areas as well. Um, always improvement, right, in all technologies, especially with AI, because you know, this folks always think it's gonna take over their jobs too, but hopefully it can just be used for good.

And that's the key thing, is in what we're trying to drive here as well. Yeah. I think the whole thing, like, is AI going to take my job is it's a nuanced conversation because a job is a series of tasks, right? Yeah, yeah. You know, you don't just do one thing anymore.

Um at the very least, you at least have to attend meetings about the one thing that you do. Uh so AI is really good at tasks. It's still good at being very narrow, at least until um IBM perfects quantum computing and they can become simple. Uh it's a little ways away. But right now, it's it is very much good at taking tasks, and the more repetitive a task is, the easier it is to automate.

The good news is that a lot of those tasks that are so easy to automate, you don't really want to be doing anyway. I used to work at a a PR firm, and uh this is one task a junior person had, they were copying and pasting results from Google to a spreadsheet eight hours a day. I'm like, how have you not clawed your eyeballs out by now? No, that would used to be my role there. So it's like, why are you doing this?

This is something that a machine should be doing. It is so repetitive, and the human there adds so little value. Um, that the goal then is to say, okay, you we're gonna save you seven hours and 45 minutes of your day, but now we want you to use what you're good at, creativity thinking across domains and stuff to add value to this instead of just copying and pasting spreadsheets. So there's this concept. Oh gosh, from the 1930s, Joseph Schumpeter, uh the idea of creative destruction.

That yes, things like AI absolutely will destroy certain tasks, but in doing so, they will create new opportunities. Um that were will ideally be better. Nobody enjoys getting a saw, going out to lake and sawing up ice in the wintertime, right? Nobody enjoys that. Exactly.

And no one predicted AI back in back then, right? So that leaves as AI might take over some things, that leaves folks for innovation and other things that we might not even know could be possible in the future. So, with that, I mean, Chris, it's been a great conversation. Um I mean, um thank you for hosting us. Uh, thank you.

If are there any last words you want to say before I share some of the resources and the description? I would encourage people to at least start if you haven't already started thinking about the applications of automation for even just you know, not AI, but just automation. We are entering a period of time now where productivity is sort of the the golden calf that everybody's looking for in business. Um, even if your company doesn't have a uh enterprise wide strategy for AI, um, you as a business person should be thinking about how do we implement even on a small scale, piloting it. You know, you can sign up for uh an IBM uh cloud account and try out Watson Studio.

Um you I believe you get 50 CPU hours per month, uh, which is enough to test some stuff out. It's not enough to you know to run an enterprise uh wide thing, but you can start testing it out. There are so much good open source uh in R in Python and learning those languages. Uh if you go to um cognitive class.ai, that's IBM's free online university to learn big data, data science, machine learning AI. It's an incredible resource, completely 100% free.

You even get cool little badges that can go on your LinkedIn profile. Um, and I I think getting your your toes wet in this stuff is is so important, just so you know what's possible. And the more people try it out, I think the the better it is for everyone because it helps to demystify it. It's not magic, it's just a bunch of math. So, Lauren, what are some of the resources you wanted to talk about?

Yeah, so you know, Think2021 is coming up. Of course, it's not our great event that used to be in person that I love, but it is virtual. So it's coming up May 11th in the Americas and May 12th in APAC Japan and EMEA. Um, you can also sign up for part two of our What's Next in AI webinar series. Um, that's something we've been working through IBM.

Uh this is on May 19th, and that will dive in a little bit more with some of our um experts on the IBM side, some of the product marketers, um just the importance, what's next, how you can improve your own AI strategy. And then um finally and not last, uh the Gardner Report. Uh this one goes into our how Watson Studio is one of the leaders in my machine learning and data science capabilities. So just so you can see, you know, what the analysts have to say, but you know, it's been a pleasure. And I think just I think what you said, we need to just get uh companies just need to use uh AI responsibly.

And I think consumers need to also ensure that these companies do get held to those standards as well so it'll be um cool to see how it goes going forward especially improvements in AI as well exactly and if you have uh any interest in it uh we have a course as well that is not free uh go to trust insights.ai slash data science one on one if you're so interested um thanks for watching today if you'd like to share this episode go to the link that uh that you probably found it by uh trust insights.ai slash IBM trusted AI. That link will take you back to the recording of this show. You can share it with your friends uh please do we'd love to to have more people uh get a sense of what's important about AI uh and making sure that it's fair making sure that we're all working towards outcomes that are equitable for everybody. Thank you Lauren and the IBM team for for being with us today and we'll talk to you soon. Uh take care everyone.

Thanks happy Friday