Technology as a force for good

Here’s a bonus episode this week from our friends behind Me, Myself, and AI https://sloanreview.mit.edu/audio-series/me-myself-and-ai/ — a podcast on artificial intelligence and business, and produced by MIT Sloan Management Review and Boston Consulting Group. We partnered with them to help promote their awesome podcast. We hand picked this full-length episode to share with you because of its focus on using technology as a force for good, something we’re very passionate about. This episode features, Paula Goldman, Chief Ethical and Humane Use Officer at Salesforce, and the conversation touches on some interesting topics around the role tech companies play in society at large. Featuring: Paula Goldman – Website https://www.salesforce.com/, LinkedIn https://www.linkedin.com/in/paula-goldman-b79ba12, X https://x.com/pdgoldmanSam Ransbotham – X https://x.com/ransbothamShervin Khodabandeh – Website https://www.bcg.com/about/people/experts/shervin-khodabandeh, LinkedIn https://www.linkedin.com/in/shervinkhodabandehShow Notes: Subscribe to Me, Myself, and AI https://sloanreview.mit.edu/audio-series/me-myself-and-ai/ on Apple Podcasts https://podcasts.apple.com/us/podcast/me-myself-and-ai/id1533115958, Spotify https://open.spotify.com/show/7ysPBcYtOPVgI6W5an6lup, or Google Podcasts https://podcasts.google.com/feed/aHR0cHM6Ly9tZW15c2VsZmFuZGFpLmxpYnN5bi5jb20vcnNz. Upcoming Events: Register for upcoming webinars here https://practicalai.fm/webinars!

• 0:03Speaker 00

  What's up practically listeners, Adam Stokoviac here, editor in chief of Change Law. We partner with our friends behind the podcast, Me, Myself and AI, to share a full length episode with you. It's a podcast on artificial intelligence and business and is produced by our friends at MIT Sloan Management Review and Boston Consulting Group. We handpicked this episode because of its focus on using technology as a force for good, something we are very passionate about. This episode features Paul Goldman, she's the chief ethical and humane officer at Salesforce, and the conversation touches on some interesting topics around the role tech companies play in society at large. Me, Myself and AI is available wherever you get your podcasts, just search Me, Myself and AI to subscribe. All right, here we go.

• 0:49Speaker 01

  AI ethics are easy to espouse, but hard to do. How do we move from education and theory into practice? Find out today when we talk with Paul Goldman, chief ethical and humane use officer at Salesforce.

• 1:02Speaker 01

  Welcome to Me, Myself and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of information systems at Boston College. I'm also the guest editor for the AI and Business Strategy Big Idea Program at MIT Sloan Management Review.

• 1:23Speaker 02

  And I'm Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG's AI practice in North America. And together, MIT SMR and BCG have been researching AI for five years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities across the organization and really transform the way organizations operate.

• 1:51Speaker 01

  Today, we're talking with Paula Goldman, Chief Ethical and Humane Use Officer at Salesforce. Paula, thanks for taking the time to talk with us today. Welcome.

• 1:58Speaker 03

  Thank you. I'm really excited to have this conversation.

• 2:01Speaker 01

  Hi, Paula.

• 2:02Speaker 03

  Hi.

• 2:03Speaker 01

  Our podcast is Me, Myself and AI. Let's start with a little about your current role at Salesforce. First, what does Salesforce do and what is your role?

• 2:11Speaker 03

  Salesforce is a large enterprise technology company that if I had to really summarize what we do, we put out a lot of products that help our customers, which tend to be companies or sometimes nonprofit organizations or whatnot, connect better with their customers, their stakeholders, whether that's from a sales process, service, marketing, you name it. And data plays a really, really important role, like understanding, giving them the tools to understand their customers and what they need and serve them better. Within that, my role, I'm Chief Ethical and Humane Use Officer, which I know is a bit of a mouthful. It's a first of its kind position for Salesforce.

• 2:52Speaker 03

  And I work with our technology teams and more broadly across the organization on two things. One is as we're building technology, thinking about the impact of that technology at scale out in the world, trying to avoid some unintended consequences, trying to tweak things as we're building them to make sure that they have maximum positive impact. And then secondly, we work on policies that are really about the use of our technology and making sure that we are putting sufficient guardrails to make sure that our technology is not abused as it is used out in the world.

• 3:24Speaker 01

  Tell us about your background. How did you end up in that role? Pretend you're a superhero. What's your origin story here?

• 3:30Speaker 03

  Well, I guess, you know, I have the short story and the long story. The short story is essentially, I am super passionate about how technology can improve people's lives. And I spent a long time thinking about the tech for good side of that. Like, I worked for a while under Pierre Omidyar, the founder of eBay, working to build an early-stage investing practice that was investing in startups that use technology to serve underserved populations, whether that meant like getting, you know, financial services to people that otherwise would be excluded in emerging markets, alternative sources of energy or whatnot. Having done that for a long time, I think...

• 4:14Speaker 03

  We started to see this shift in the role of technology in society. For a long time, I think the technology industry viewed itself as a bit of an underdog, a disruptor. And then all of a sudden, you could sort of look and see the writing on the wall. And technology companies were not only the biggest companies in whatever financial index you want to name, but also technology was so pervasive in every aspect of all of our lives. And even more so because of COVID. And I think we just sort of saw the writing on the wall and saw that the sort of famous adage with great, oh, I'm going to mess this up. With great power comes great responsibility.

• 4:59Speaker 01

  Perfect tie into my superhero origin question, because that's the Spider-Man quote. Go ahead.

• 5:05Speaker 03

  It's time to think about guardrails, particularly for emerging technologies like AI, but kind of across the board, how to think about what do these technologies do at scale? And like any industry that goes through a period of maturation, that's where I think tech is. That's kind of like my motivation around it. And as part of that role, I was leading a tech ethics practice. I was asked to be on Salesforce's ethical use advisory board. And through that, they asked me to come lead this practice.

• 5:31Speaker 02

  Paula, your background's generally been quite focused on this topic, right? I mean, even before Salesforce.

• 5:38Speaker 03

  That's right. You know, that's a bit of what I was referring to. I spent a lot of time, I mean, even just straight from college, I spent a lot of time on mission-driven startups, often very technology-driven, that were meant to, again, open up opportunity. For example, I spent a lot of time in India, working with an affordable private school, but like thinking again about how does technology open up opportunity for these students? And many years later, I won't tell you how many years later, a lot of those students are actually working in technology in the country. Essentially, it's been a through line in my work, the role of technology and markets as a force for good. How do we implement also appropriate guardrails?

• 6:22Speaker 03

  And think about the power of trust in technology, which is ultimately so essential for any company that's putting out a product in the world these days.

• 6:33Speaker 01

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• 7:07Speaker 02

  How do you think the nature of the dialogue has changed since you've been in this field for some time as one of the pioneers in this area? How do you think the nature of the dialogue has changed? Let's say from 10 years ago.

• 7:20Speaker 03

  Yeah.

• 7:21Speaker 02

  You know, guardrails and ethical use versus now?

• 7:26Speaker 03

  I would say 10 years ago, and let's start with giving credit where credit is due. 10 years ago, certainly there was a ton of leadership in academia thinking about these types of questions. And I think if you would go to a campus like MIT, you'd find a lot of professors teaching classes on this and doing research on this. It has been a longstanding field, society and technology, science and technology, call it what you will, and many other disciplines. But I don't think it was as widespread of a topic of public conversation. And today, you could hardly pick up a newspaper without seeing a headline about some sort of technology implication, whether it's AI or a privacy story or a social media story or whatnot. And certainly, it was fairly rare 10 years ago to think about companies

• 8:11Speaker 03

  hiring people with titles like mine. I think about that history. I actually think a lot about the analogy with the security industry went through, you know, let's call it in the 90s with security, right? So what the technology industry went through in the 90s with security. There too, it was a fairly immature field. And you might well, as an observer at that point, have looked at the viruses that were attacking technology. And thought, how could you possibly predict every risk and get a framework for getting ahead of it and fast forward to where we are now? And it's a mature discipline. Most companies have teams around this and sets of protocols to make sure that their products don't have these vulnerabilities.

• 8:52Speaker 03

  And I think we're kind of in the early stages of a similar evolution, especially with AI and AI ethics, where these sorts of norms will become standard. And this is a specialized profession that's developing.

• 9:06Speaker 01

  Maybe let's get a little bit more specific here. And we've talked about needing guardrails. What kinds of things do people need to be worried about?

• 9:13Speaker 03

  If we zoom in on AI specifically here, the positive potential impact and real, actually, real time impact of that technology is immense. Right? So think about healthcare. One of our research teams is working on AI to spot breast cancer with machine learning. It's called ReceptorNet. That's the tip of the spear. There's so much stuff going on in healthcare that can improve outcomes and save lives. We have a project called SharkEye, which is using vision to look at images of beaches and see if there's a shark there for a safety warning. Right? There are many, many, many applications of AI like that, that have huge benefits

• 9:58Speaker 03

  for humanity. At the same time with technologies, there's often unintended consequences that come. AI is really an automation of human intelligence and it's as good as the data that it gets fed. And that data is the result of human decisions and it makes it imperfect. That's really important for us to look out for. It's very, very important for companies that are using AI to automate processes or especially make decisions that could impact human outcomes, whether that's a loan or, you know, an access to a job or whatnot. I'm sure by now you've heard many times about the research that was done, I think

• 10:43Speaker 03

  actually partly at MIT about facial recognition by folks like Joy Bellemwini and Timnit Gubru and showing that facial recognition is more accurate on lighter skinned people versus darker skinned people, which can have catastrophic impacts if it's in a criminal justice setting. There's a lot of stuff to look out for to make sure that, in particular, that the questions of bias are appropriately safeguarded when developing this technology.

• 11:11Speaker 02

  On that point, clearly, there is a strong case to be made to make sure that, as you said, the unintended consequences are understood and mitigated and managed. That is only going to get more complex as AI gets smarter and there will be more data. Do you think that there is a possibility of AI itself driving or being a contributor to more ethical outcomes? Or to more equity in certain processes? I mean, there's, there's clearly a case for making sure AI doesn't do something crazy.

• 11:56Speaker 02

  And then is there also possible for AI to be used to make sure we humans don't do something crazy?

• 12:02Speaker 03

  Of course, I think that's the flip side that maybe doesn't get talked about as much. But humans making decisions about who gets a loan or who gets a job are also very subject to bias. So I think there is the potential, if done right, when AI is used in those circumstances in combination with human judgment and appropriate guardrails for the three of those things to actually open up more opportunities together. And I'm just giving examples of, of use cases, but I think that's probably across the board. Going back to the healthcare example, a doctor could be tired the day he's looking at a scan for cancer.

• 12:43Speaker 03

  And that's why, you know, sometimes we get into these polarized discussions of AI versus humans. And it's not an either or, it's an and, and it's with a set of guardrails and responsibilities.

• 12:59Speaker 02

  I wanted to do a follow up on the guardrails and responsibilities. And as you're thinking about ethical AI, either for Salesforce or more broadly for any organization.

• 13:11Speaker 02

  How much of the effort to do this at scale do you think is about guardrails and education and governance and more visibility and appreciation of the potential risks?

• 13:31Speaker 02

  So it's process and people kind of stuff versus technology itself?

• 13:38Speaker 03

  Absolutely. And when you think about, part of the answer to these guardrails is the technology itself. How do you build tools at scale to watch for risk factors? And this is actually something we try to do with our customers, right? So we have, we have integrated AI into a number of applications. For example, we have chatbots for customer service and we have AI that helps salespeople with lead and opportunity scoring so that they know which prospect to go after and can spend most of their time going after that prospect. And within that, we've built technological safeguards and prods. Some of it is just the way we build the technology itself, but some of it is

• 14:22Speaker 03

  actually having the technology prompt the human and say, for example, hey, you're building a model. You have identified that maybe you don't want race as a variable in this model because it can introduce bias, but we see here you have a field that is zip code and zip code can be highly correlated with race. I think the answer to your question is yes. There's a very human element to this question, but to address this at scale, you actually need to automate the solution as well. And again, it kind of comes back to what we were just saying of combining the technology, the human, the process, the judgment.

• 15:03Speaker 03

  All together to solve the problem. At Salesforce, we do something called consequence scanning, building off of a methodology put out by a nonprofit in the UK called dot everyone. And we've kind of customized it for our own good. We're about to put a toolkit out around it. But we work with Scrum teams at the beginning of a process. And we say, it's actually kind of simple at the end of the day. Hey, what are you intending to build here? And what might some of the unintended consequences be? Positive and negative? And from that, we generate ideas that actually go on the backlog for that team. And that's how you sort of influence the, the product roadmap. It's not foolproof for sure. And you're not going to

• 15:46Speaker 02

  But it's getting better.

• 15:47Speaker 03

  It's getting better as you might expect it to, as you start to see some of these consequences at scale. And you start to see the sort of pushback and critique from society. And it's always more complicated than, you know, a sort of black and white picture of how simple it might be to fix things. It's not simple to fix AI bias. But there's also no excuse for not paying attention to it now because it's such a known problem.

• 16:14Speaker 02

  You know, you commented on the past in this field and how the evolution and strength of technology and tech firms has shifted the dialogue. And you've talked a lot about the present. How do you think the future will be 10 years from now? How do you think this conversation we're having now, which 10 years ago wasn't commonplace? That, hey, let's look at the algorithms. Let's look at unfair treatments. Let's get zip codes, whether it's a correlation to poverty levels or race. How do you think the future will be different?

• 16:49Speaker 03

  Well, you're catching me on a good day, so I'm optimistic about this. But really, I don't think these are easy things to scale. Not in the beginning, as in any sort of big change in industry. I think all the signs do point to the maturation of this type of work. I will say, especially with, you know, more obvious places like AI or crypto or, or, you know, things that are kind of in the news because of the questions that come up around them. I will also say there's a lot of regulatory pressure and some of that, how that will play out will depend on a lot of very complicated politics.

• 17:30Speaker 03

  But you see it not only in the US, you see it in Europe that just released a draft AI law a couple of months ago and in other jurisdictions as well, also privacy legislation, social media legislation. And those debates are bringing to the forefront, what is the responsibility of technology companies versus governments and the role that civil society can play? And I think you could pick apart any particular proposal with pros and cons, but the debate itself is very healthy in large part. And I take a lot of hope from that because if you were to ask me what's going to happen with technology companies on their

• 18:13Speaker 03

  own, I actually don't think it would scale on its own, right? It's part of an ecosystem where companies play a role, civil society groups play a role, that voice is very, very important. And it's the combination of all those things, which I will say actually has surprised me in terms of how robust it's remained. And the conversation keeps deepening and widening.

• 18:36Speaker 01

  Well, okay, this is great. Enough about you, but I can focus on me for a minute. I teach a bunch of, I teach a bunch of college students. Let's talk about you, Sam. I teach a bunch of college students. That seems like a way to influence the future. What should we be telling people? I mean, I have, Monday morning, I have a class on machine learning and artificial intelligence. What can you vicariously tell me to tell them? What should we be talking about?

• 18:58Speaker 03

  Well, what have you told them? I'm curious. Has it come up?

• 19:01Speaker 01

  Oh, it comes up all the time. Actually, I feel a little bit negative because I always start the, you know, every class, even though it's a technical class, we start with what's happening in the news. And I feel evil because The news is lots of times either glorious example of something that could happen in a lab in 30 years, or something terrible that's happening right now.

• 19:21Speaker 03

  Right.

• 19:22Speaker 01

  And I feel like I come to the dark side. So what should we be telling college students? Are people heading into this field?

• 19:28Speaker 03

  I'm going to go on a little bit of tangent and then come back to your question. I think your commentary in the news is really apt, and it does relate to the conversation about technology, because it is very extreme right now. It's all the things that are wrong, which we should talk about for sure. And then all these kind of overstated claims about what will happen in 30 years, but ignoring that there's a lot of just extraordinary benefit right now also happening from technology. And I could go on and on about this, you know, thinking about what technology did for society during COVID, how it helped businesses stay open and people stay safe, and on and on. I think there is a more nuanced conversation

• 20:05Speaker 03

  that balances the real need for caution and societal engagement. You know, at the end of the day, when you get this right, technology is an extraordinary force for opening up opportunity for people. And I think that kind of basic thinking about that balance in how we teach up and coming people who may indeed work in technology is very important. I will also say I've been super heartened over the last years to see the blossoming of curricula that are around tech ethics, especially in the university setting. And I remember a few years ago, when I was at Omidyar Network, we sponsored a sort of

• 20:50Speaker 03

  challenge for educators and professors to work this into CS curriculum. We see that blossoming all over the place, a lot of integration across different technological disciplines. And that's super heartening, because then it's sort of part of the conversation. You know, the real challenge you want to avoid working in a company is that this is seen as some effort, often a corner, separate from the real business.

• 21:21Speaker 01

  Governance.

• 21:21Speaker 03

  Exactly. You want technologists to see this as part of their job. It's so cool that we're seeing more and more ways that professors are just integrating this into the standard curricula of name your technological discipline.

• 21:39Speaker 01

  One of the things that I like to do in class is give people data sets and then say that, "Hey, I don't know, that column, I think I told you it was X, it actually is race." How does that change what you feel about the analysis you just did and how proud you were of the significant results you got based on using that variable? And does it matter and why?

• 21:57Speaker 03

  Does it work?

• 21:58Speaker 02

  That's a great educational tool.

• 22:00Speaker 01

  Yeah, I think it does work because it says, "Well, hey, you're just treating this as data, but this data actually represents something in the real world that is an attribute of a real human versus an abstract number in row seven and column three."

• 22:16Speaker 03

  That's another huge topic, right? And another really important thing to set when we're teaching is that data is a person, most likely. And keeping that in mind as we're thinking about what data we're collecting, what our intended use is, whether we really need that data, super, super important. Something that we also spend a lot of time thinking about within our overall tech ethics work at Salesforce.

• 22:44Speaker 01

  So when you're hiring people, what do you want them to know? What kind of skills are people needing? How can you tell that someone's going to use their power with great responsibility, to come back to your prior quote?

• 22:54Speaker 03

  This is a great one. It's something that DJ Patel, who's the former chief technologist at the United States, he talks about this a lot too, in the context of data science. In the hiring process, when you're asking someone, so often hiring processes will have an exercise that they'll ask someone to do. Throw in an ethics question, see how they deal with it, see how coherent the answer is. I think those types of little cues, they not only help you evaluate how sophisticated someone's thinking is about those questions, but those cultural cues cannot be underestimated. And we think about that a lot as well. How do we create a culture in which everyone feels like it's their responsibility to think about these questions?

• 23:37Speaker 03

  And part of that is about giving them the tools that they need to do it and the incentives that they need to do it, which include having this be echoed all around by leadership. It's like, how often are you coming up, running across these questions? Whether it's an interview process, or it's in an all hands, or it's in your one-on-one with your manager? It matters a lot, which brings me back to why I'm so excited that this is starting to really flourish in university settings in the teaching itself, you know, in the core curriculums, like it's, those cues matter a lot.

• 24:12Speaker 01

  Paul, this is all quite fascinating. I think, you know, if we come back to what Sherwin kind of instigated beginning was how technology can affect society and can affect the organization as a whole, not so much, you know, lots about how we use technology, but these technologies affect us and affect society. And it's notable that those effects can be positive in value building or negative. And it just depends on how we make those choices to use it. Thank you for taking the time to talk with us. I've really enjoyed it. Yeah, thank you so much. Very insightful.

• 24:44Speaker 03

  It was super fun. Thank you for having me.

• 24:46Speaker 01

  Coming up in our next episode, we'll speak with Barbara Martin Coppola, Chief Digital Officer at IKEA. Please join us.

• 24:54Speaker 04

  Thanks for listening to Me, Myself and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders. And if you join us, you can chat with show creators and hosts, ask your own questions, share your insights, and gain access to valuable resources about AI implementation from MIT SMR and BCG. You can access it by visiting mitsmr.com/AIforleaders. We'll put that link in the show notes and we hope to see you there.