Speaker 1: This is Bloomberg Business Week with Carol Messer and Bloomberg
Speaker 1: Quick Takes Tim Stinovic on Bloomberg Radio. Hey listen. AI.
Speaker 1: Artificial intelligence not the thing of science fiction. It is
Speaker 1: all over our world, already determines who we hire, what
Speaker 1: we might invest in or buy, manager supply chains, provides governance.
Speaker 1: It is everywhere. Last week I d C noting that
Speaker 1: spending on AI solutions alone doubling in the US by
Speaker 1: Tim two billion dollars. Pretty massive market and a massive
Speaker 1: opportunity I think for a lot of companies. Bena Amanath
Speaker 1: is Executive Director of Global Deloitte AI Institute, head of
Speaker 1: Trustworthy AI and Ethical Tech at Deloitte as well. Joining
Speaker 1: us now on the zoom from California. She's got a
Speaker 1: new book out today. It's called Trustworthy AI, a Business
Speaker 1: Guide for Navigating trust and Ethics in AI. So being
Speaker 1: a give us an idea of you know, I think
Speaker 1: before we actually get to the ethics AI that the
Speaker 1: part of this that your book focuses on. I want
Speaker 1: to talk a little bit about where where consumers experience AI,
Speaker 1: Where are audience would actually interact with AI without even
Speaker 1: knowing it? Everywhere if they're using a smartphone, they're using AI,
Speaker 1: and which is the most prominent use of AI today
Speaker 1: without your knowing that everything that they use, right from
Speaker 1: their maps to the messaging to the social media, banking,
Speaker 1: everything is powered by AI today and it's in their pockets,
Speaker 1: in their phone right now. So it's a good thing.
Speaker 1: I say that with a little sarcasm. I mean, how
Speaker 1: do we like? It could be very helpful in supply
Speaker 1: chain management if you think about it, in governance. I
Speaker 1: remember doing some stories about venture capitalists being able to
Speaker 1: look at uh, smaller deals that maybe wouldn't make sense
Speaker 1: to spend time on, but using technology, using AI, they
Speaker 1: can kind of screen through things. So how do you
Speaker 1: think about AI? The good, the bad, and the ugly.
Speaker 1: That's a great question, Carol, And you know, I think
Speaker 1: there's a lot of goodness that AI can bring to
Speaker 1: humanity as a whole, but there are side effects as well.
Speaker 1: And today we are in that era of it. Everybody
Speaker 1: is focused on the value creation of AI, and we
Speaker 1: see these high level headlines on all the bad things
Speaker 1: AI is doing. The reality is that it's a balance.
Speaker 1: There is a lot of goodness and there's a lot
Speaker 1: of value positive value things what you mentioned, supply chain optimization, translation,
Speaker 1: patient diagnosis. There's a lot of goodness that's coming out,
Speaker 1: but there's also unintended consequences that's coming into the to
Speaker 1: the forefront as AI is scaling out into more and
Speaker 1: more applications. Okay, I want to get to some of
Speaker 1: those unintended consequences, but I also have you know, I
Speaker 1: think a lot of I come from a place that
Speaker 1: a lot of our listeners and viewers can relate to.
Speaker 1: But you know, the idea when you're on the phone
Speaker 1: trying to tall an airline, for example, or perhaps get
Speaker 1: something fixed, and you're you know, you have AI trying
Speaker 1: to work with you, and like it's like, you know,
Speaker 1: represent you just keep repeating representative over and over again,
Speaker 1: right because the bond just isn't doing it for you.
Speaker 1: And I think that's frustrating for a lot of consumers.
Speaker 1: That's true, Tim, I'm with you. I had a lot
Speaker 1: of challenge, especially with my accent, which doesn't fit into
Speaker 1: the norm right to get communicate anything. And you know,
Speaker 1: at some point you're just yelling representative, right. So A look,
Speaker 1: the reality is that technology is still way in its infancy,
Speaker 1: right because the research is still happening. It's not a
Speaker 1: fully developed technology, but using it in the real world.
Speaker 1: Since it's not fully developed, there are that unintended consequences
Speaker 1: that not that's not well thought through. So you're using
Speaker 1: think of it, you're using something that is not fully mature, right,
Speaker 1: and it is growing along with you while you're using it.
Speaker 1: It's learning from you. It's learning my accent and adopting
Speaker 1: it and in the next operation somebody with a similar
Speaker 1: accents by it will be easier to recognize. So it
Speaker 1: is learning and growing, and that's why there's an element
Speaker 1: of patients that needs to come into play because it's
Speaker 1: not being trained for every possible scenario. You want to
Speaker 1: get back to her Guest Puma is executive director Global
Speaker 1: Deloite AI Institute, Head of Trustworthy AI and Ethical Tech
Speaker 1: at A Lloyd and the book she's got out Trustworthy,
Speaker 1: a Business Guide for Navigating trust in Ethics and AI.
Speaker 1: I want to get back to transparency or bring that up.
Speaker 1: But you know, let me ask you, is AI what's
Speaker 1: going to determine in a self driving car whether or
Speaker 1: not you hit the dog, the cat, or the cyclist
Speaker 1: or are none hopefully right? Thank you Tim, That's that's
Speaker 1: a great question, and I think you know it is
Speaker 1: at this point it is really up to that the
Speaker 1: company who's creating the self driving car that was determining
Speaker 1: it until we get regulations in place, And my book
Speaker 1: really gives that company a structure to think about it
Speaker 1: and offer potentially offer those options to the consumer or potentially,
Speaker 1: you know, bring together a focus group to decide it.
Speaker 1: But there needs to be a structured way of thinking it,
Speaker 1: unlike just building that technology and deploying it out into
Speaker 1: the world and then you know, seeing what happens. So
Speaker 1: I think there needs to be more mindfulness, more thought
Speaker 1: put into what are the possible scenarios and how do
Speaker 1: you mitigate those? What are the questions that executives come
Speaker 1: to you with when it comes to AI, like what
Speaker 1: are their goals uh in sort of building out projects
Speaker 1: that rely on AI technology? And then what do you
Speaker 1: have to tell them in terms of guard rails and
Speaker 1: in terms of making sure they don't make mistakes of
Speaker 1: the past. That's a great question, Jim, and you know
Speaker 1: many of them are just looking for how can I
Speaker 1: get value from AI from my business? And there are
Speaker 1: two options. One is either through new revenue opportunities, meaning
Speaker 1: building new AI products that couldn't be done with existing technology.
Speaker 1: So there's put a brand new revenue opportunities. Or the
Speaker 1: other is optimizing or cost savings. Right, how do you
Speaker 1: optimize your current supply chain process for example to get
Speaker 1: better value for the goods that you buy from your sourcers?
Speaker 1: How do you optimize your document management for example? So
Speaker 1: there are those are the two you know ways you
Speaker 1: can get value for from AI for your business today,
Speaker 1: and most of them at that point are not thinking
Speaker 1: about guard rails or the ways could go wrong. So
Speaker 1: really it is up to us as the general audience,
Speaker 1: as the public, and definitely mean my role is to
Speaker 1: raise that awareness. Look, you're focusing on all the positive
Speaker 1: value creation, but there are risks associated with it. The
Speaker 1: obvious ones that the brand and reputational risk, but there
Speaker 1: are really bad things that can happen if you do
Speaker 1: not think about the ethical implications up front and put
Speaker 1: in those guard rails. Like like, what's the thing that
Speaker 1: you that you worry most about in terms of AI
Speaker 1: and maybe the misuse or the unethical impact of it.
Speaker 1: The thing I worry most about is that we are
Speaker 1: not thinking about the ways it could go wrong, and
Speaker 1: you can take any scenario right and it is. The
Speaker 1: challenge is that it's not a one size fits all.
Speaker 1: There is no single answer. Like you think of something
Speaker 1: as you know, as simple as personalized marketing, right where
Speaker 1: you are taking consumer data, matching it with products and
Speaker 1: providing personalized ads. Right, But that same person, that same technology,
Speaker 1: the AI can be used for providing for potentially personalized healthcare.
Speaker 1: Now when you think about bias in this scenario, the
Speaker 1: bias in the personalized marketing world means you know, the
Speaker 1: wrong add to the wrong person, but in the healthcare
Speaker 1: space it means a wrong diagnosis to the wrong person,
Speaker 1: which is terrible, right, So I think it has to
Speaker 1: be weighted. So what I try to make sure is
Speaker 1: you think about the nuances the context, because AI as
Speaker 1: a whole is about intelligence, and intelligence is different based
Speaker 1: on the scenario, the solution, the industry that you're in.
Speaker 1: It is not going to be a one size fit
Speaker 1: at all. So how do you weigh it as a
Speaker 1: CEO to say which other risks were I'm willing to
Speaker 1: take and which other is I'm not willing to take?
Speaker 1: And that I need to think more about. I'm interested
Speaker 1: in when you know you you have a background in
Speaker 1: this is you know that engineering um background in obviously
Speaker 1: in technology. And I'm wondering from consumer side, not from
Speaker 1: the consultant side. When you've been out in the world
Speaker 1: and you've encountered AI and you've thought to yourself, this
Speaker 1: is exactly the right way that this company should be
Speaker 1: using AI. What's an example of that? Well, you know,
Speaker 1: I think the ones that that I struggle with most
Speaker 1: are the you know, where there is a human impact.
Speaker 1: And I'll give you two scenarios. You know, let's take
Speaker 1: facial recognition. Everybody has heard about it and has heard
Speaker 1: all the ways it could go back. Right, So, facial
Speaker 1: recognition as as a technology, when it's biased and used
Speaker 1: in a law enforcement scenario, it's a terrible thing. But
Speaker 1: there is also facial recognition being used by startups to
Speaker 1: recognize human trafficking victims. Right. And look, I gotta tell
Speaker 1: you too. Recently, My parents recently flew internationally and I
Speaker 1: talked to them and they told me that in order
Speaker 1: to board the plane, to order to board the plane,
Speaker 1: they just walked on because they didn't have to give
Speaker 1: their boarding past to any When the facial recognition recognize them.
Speaker 1: I think exactly cool. That's a great scenario that who
Speaker 1: we're creepy, Like they don't like creepy stuff right there,
Speaker 1: like you know, kind of late adopters. They like the
Speaker 1: ease of it. Yeah, they like the ease of it. Yes.
Speaker 1: The coolest one I've heard with facial recognition is when
Speaker 1: you're using it at traffic light to recognize human trafficking victims.
Speaker 1: And that's why that nuance comes in. Right, Yes, the
Speaker 1: technology might be biased, but if it is helping you
Speaker 1: rescue eighty percent more victims then you could without AI,
Speaker 1: then that's a decision that you know, the organization and
Speaker 1: we need to make right. It is biased, but it
Speaker 1: is still helping rescue eighty person more with Without it,
Speaker 1: you would not get that eighty person of the victims rescue, right,
Speaker 1: So it's an informed decision on Yes, we know it's biased,
Speaker 1: but we're still going to use it in this scenario
Speaker 1: because it's helping us. But you know, in a law
Speaker 1: enforcement scenario, even if it's just eighty percent, you know,
Speaker 1: curate it's a terrible thing. You cannot use it, right,
Speaker 1: So it depends on the scenario in which you're using
Speaker 1: it the example you gave about your parents being able
Speaker 1: to walk in right, it's the great rus right, what's
Speaker 1: the worst case that can happen? Pebody, Yeah, it just
Speaker 1: kind about fifty seconds left here. I mean, we've already
Speaker 1: given up a fair amount of privacy right because of AI,
Speaker 1: and we probably will continue to do so. Yes, Yes,
Speaker 1: And the reality is the definition of privacy has really
Speaker 1: changed in the last two and a half years. Before UNIT,
Speaker 1: it was all about all data sharing, data usages terrible.
Speaker 1: You know, privacy needs to be protected. But what has
Speaker 1: happened with contact tracing? If you don't share your data,
Speaker 1: it's actually bad, right. So I think the evolving definitions
Speaker 1: of some of these dimensions of trustworthy AI is going
Speaker 1: to continue to evolve and shape. So we just have
Speaker 1: to be agile about it. Wow, a lot of issues
Speaker 1: and certainly something we've scratched the surface that feels like
Speaker 1: and certainly something that will be a bigger part of
Speaker 1: our conversation as I just kind of invades all parts
Speaker 1: of our world in a good way, But there's also
Speaker 1: things to be concerned about. As you just laid out. Beina,
Speaker 1: I'm a math. She is executive director at Global Deloitte
Speaker 1: AI Institute, Head of Trustworthy AI and Ethical Tech at Deloitte.
Speaker 1: Her new book is Trustworthy AI, a Business Guide for
Speaker 1: navigating trust and ethics and AI. A lot of really,
Speaker 1: a lot of fascinating conversations, like conversation to be had
Speaker 1: around AI and treading and to understand like where we're
Speaker 1: using it in the background right exactly