Managing Risk in a World Where AI is Everywhere

opportunity I think for a lot of companies. Bena Amanath is Executive Director of Global Deloitte AI Institute, head of Trustworthy AI and Ethical Tech at Deloitte as well. Joining us now on the zoom from California. She's got a new book out today. It's called Trustworthy AI, a Business Guide for Navigating trust and Ethics in AI. So being a give us an idea of you know, I think before we actually get to the ethics AI that the

part of this that your book focuses on. I want to talk a little bit about where where consumers experience AI, Where are audience would actually interact with AI without even knowing it? Everywhere if they're using a smartphone, they're using AI, and which is the most prominent use of AI today without your knowing that everything that they use, right from their maps to the messaging to the social media, banking, everything is powered by AI today and it's in their pockets,

in their phone right now. So it's a good thing. I say that with a little sarcasm. I mean, how do we like? It could be very helpful in supply chain management if you think about it, in governance. I remember doing some stories about venture capitalists being able to look at uh, smaller deals that maybe wouldn't make sense to spend time on, but using technology, using AI, they can kind of screen through things. So how do you think about AI? The good, the bad, and the ugly.

That's a great question, Carol, And you know, I think there's a lot of goodness that AI can bring to humanity as a whole, but there are side effects as well. And today we are in that era of it. Everybody is focused on the value creation of AI, and we see these high level headlines on all the bad things AI is doing. The reality is that it's a balance. There is a lot of goodness and there's a lot of value positive value things what you mentioned, supply chain optimization, translation,

patient diagnosis. There's a lot of goodness that's coming out, but there's also unintended consequences that's coming into the to the forefront as AI is scaling out into more and more applications. Okay, I want to get to some of those unintended consequences, but I also have you know, I think a lot of I come from a place that a lot of our listeners and viewers can relate to.

But you know, the idea when you're on the phone trying to tall an airline, for example, or perhaps get something fixed, and you're you know, you have AI trying to work with you, and like it's like, you know, represent you just keep repeating representative over and over again, right because the bond just isn't doing it for you. And I think that's frustrating for a lot of consumers. That's true, Tim, I'm with you. I had a lot of challenge, especially with my accent, which doesn't fit into

the norm right to get communicate anything. And you know, at some point you're just yelling representative, right. So A look, the reality is that technology is still way in its infancy, right because the research is still happening. It's not a fully developed technology, but using it in the real world. Since it's not fully developed, there are that unintended consequences

that not that's not well thought through. So you're using think of it, you're using something that is not fully mature, right, and it is growing along with you while you're using it. It's learning from you. It's learning my accent and adopting it and in the next operation somebody with a similar accents by it will be easier to recognize. So it is learning and growing, and that's why there's an element of patients that needs to come into play because it's

not being trained for every possible scenario. You want to get back to her Guest Puma is executive director Global Deloite AI Institute, Head of Trustworthy AI and Ethical Tech at A Lloyd and the book she's got out Trustworthy, a Business Guide for Navigating trust in Ethics and AI. I want to get back to transparency or bring that up. But you know, let me ask you, is AI what's going to determine in a self driving car whether or not you hit the dog, the cat, or the cyclist

or are none hopefully right? Thank you Tim, That's that's a great question, and I think you know it is at this point it is really up to that the company who's creating the self driving car that was determining it until we get regulations in place, And my book really gives that company a structure to think about it and offer potentially offer those options to the consumer or potentially, you know, bring together a focus group to decide it.

But there needs to be a structured way of thinking it, unlike just building that technology and deploying it out into the world and then you know, seeing what happens. So I think there needs to be more mindfulness, more thought put into what are the possible scenarios and how do you mitigate those? What are the questions that executives come to you with when it comes to AI, like what are their goals uh in sort of building out projects

that rely on AI technology? And then what do you have to tell them in terms of guard rails and in terms of making sure they don't make mistakes of the past. That's a great question, Jim, and you know many of them are just looking for how can I get value from AI from my business? And there are two options. One is either through new revenue opportunities, meaning building new AI products that couldn't be done with existing technology. So there's put a brand new revenue opportunities. Or the

other is optimizing or cost savings. Right, how do you optimize your current supply chain process for example to get better value for the goods that you buy from your sourcers? How do you optimize your document management for example? So there are those are the two you know ways you can get value for from AI for your business today, and most of them at that point are not thinking

about guard rails or the ways could go wrong. So really it is up to us as the general audience, as the public, and definitely mean my role is to raise that awareness. Look, you're focusing on all the positive value creation, but there are risks associated with it. The obvious ones that the brand and reputational risk, but there are really bad things that can happen if you do not think about the ethical implications up front and put

in those guard rails. Like like, what's the thing that you that you worry most about in terms of AI and maybe the misuse or the unethical impact of it. The thing I worry most about is that we are not thinking about the ways it could go wrong, and you can take any scenario right and it is. The challenge is that it's not a one size fits all.

There is no single answer. Like you think of something as you know, as simple as personalized marketing, right where you are taking consumer data, matching it with products and providing personalized ads. Right, But that same person, that same technology, the AI can be used for providing for potentially personalized healthcare.

Now when you think about bias in this scenario, the bias in the personalized marketing world means you know, the wrong add to the wrong person, but in the healthcare space it means a wrong diagnosis to the wrong person, which is terrible, right, So I think it has to be weighted. So what I try to make sure is you think about the nuances the context, because AI as a whole is about intelligence, and intelligence is different based on the scenario, the solution, the industry that you're in.

It is not going to be a one size fit at all. So how do you weigh it as a CEO to say which other risks were I'm willing to take and which other is I'm not willing to take? And that I need to think more about. I'm interested in when you know you you have a background in this is you know that engineering um background in obviously in technology. And I'm wondering from consumer side, not from

the consultant side. When you've been out in the world and you've encountered AI and you've thought to yourself, this is exactly the right way that this company should be using AI. What's an example of that? Well, you know, I think the ones that that I struggle with most are the you know, where there is a human impact. And I'll give you two scenarios. You know, let's take facial recognition. Everybody has heard about it and has heard

all the ways it could go back. Right, So, facial recognition as as a technology, when it's biased and used in a law enforcement scenario, it's a terrible thing. But there is also facial recognition being used by startups to recognize human trafficking victims. Right. And look, I gotta tell

you too. Recently, My parents recently flew internationally and I talked to them and they told me that in order to board the plane, to order to board the plane, they just walked on because they didn't have to give their boarding past to any When the facial recognition recognize them. I think exactly cool. That's a great scenario that who we're creepy, Like they don't like creepy stuff right there, like you know, kind of late adopters. They like the

ease of it. Yeah, they like the ease of it. Yes. The coolest one I've heard with facial recognition is when you're using it at traffic light to recognize human trafficking victims. And that's why that nuance comes in. Right, Yes, the technology might be biased, but if it is helping you rescue eighty percent more victims then you could without AI, then that's a decision that you know, the organization and

we need to make right. It is biased, but it is still helping rescue eighty person more with Without it, you would not get that eighty person of the victims rescue, right, So it's an informed decision on Yes, we know it's biased, but we're still going to use it in this scenario because it's helping us. But you know, in a law enforcement scenario, even if it's just eighty percent, you know,

curate it's a terrible thing. You cannot use it, right, So it depends on the scenario in which you're using it the example you gave about your parents being able to walk in right, it's the great rus right, what's the worst case that can happen? Pebody, Yeah, it just kind about fifty seconds left here. I mean, we've already given up a fair amount of privacy right because of AI, and we probably will continue to do so. Yes, Yes, And the reality is the definition of privacy has really

changed in the last two and a half years. Before UNIT, it was all about all data sharing, data usages terrible. You know, privacy needs to be protected. But what has happened with contact tracing? If you don't share your data, it's actually bad, right. So I think the evolving definitions of some of these dimensions of trustworthy AI is going to continue to evolve and shape. So we just have

to be agile about it. Wow, a lot of issues and certainly something we've scratched the surface that feels like and certainly something that will be a bigger part of our conversation as I just kind of invades all parts of our world in a good way, But there's also things to be concerned about. As you just laid out. Beina, I'm a math. She is executive director at Global Deloitte AI Institute, Head of Trustworthy AI and Ethical Tech at Deloitte. Her new book is Trustworthy AI, a Business Guide for

navigating trust and ethics and AI. A lot of really, a lot of fascinating conversations, like conversation to be had around AI and treading and to understand like where we're using it in the background right exactly