Episode 10 - Looking at Technology Through the Consequence Lens with Dr. Mark Silver

00:00:17:03 - 00:00:32:03
Jie Ren
Hi everyone. Welcome to my podcast. This is Professor Jie Ren again. Today I'm very happy to have my good friend, Dr. Mark Silver to join me to talk about the consequences of adopting tech. So, Mark, thank you so much for being on the podcast.

00:00:32:05 - 00:00:33:21
Mark Silver
Oh, Jie, thank you very much for inviting me.

00:00:34:02 - 00:00:37:02
Jie Ren
Okay, so could you please introduce yourself to our audience?

00:00:37:04 - 00:00:52:18
Mark Silver
Certainly. I'm a professor of Information Technology. I was on the faculty at the Anderson Graduate School of Management at UCLA, and then the Stern School of Business at NYU, and now I am a professor, and also the Associate Dean for Undergraduate Studies at the Gabelli School, Fordham University.

00:00:52:20 - 00:01:02:21
Jie Ren
Nice. So, I'm going to ask you some questions about your personal journey pursuing tech. Okay. So, what first drew you to tech?

00:01:02:23 - 00:01:28:00
Mark Silver
That's a really good question. I can think back to when I was ten years old and someone gave me this toy computer. It wasn't electronic, but it was plastic and metal. But you really could program it. And I didn't know it then, but it really was based on the same theories that you learn in computer science. And I don't know if they gave it to me because I was already interested or if I was already¬–, if I became interested from using it.

00:01:28:01 - 00:01:42:03
Mark Silver
But ever since then, I just loved computers. Then when I was in college, I had to make a choice. I didn't go for computer science. I actually became a theoretical math major, but I worked for the university on the side doing computer programing and.

00:01:42:05 - 00:01:43:23
Jie Ren
Applied then.

00:01:44:00 - 00:02:07:08
Mark Silver
That’s exactly the word. So, when I graduated, I had to make a choice to, between theory and applied. Yes. I went with the applied. I worked for a couple of years for a startup in database management systems, a software company, and all the principals were actually Wharton professors. So, after two years, I went back to Wharton for a Ph.D. in Information Technology.

00:02:07:10 - 00:02:35:05
Mark Silver
But the thing about Wharton, in those days, they just created a new department called Decision Sciences, and they combined Information Technology and Management Science Operations Research, which is kind of the quantitative side of management and decision making and a little cognitive psychology, behavior of decision theory and a little microeconomics. And the idea was, if you really want to understand how people make decisions, you need to look at all of those things.

00:02:35:06 - 00:02:59:01
Mark Silver
Yes. So technically I'm a decision scientist, but a decision scientist who specializes in information technology. And I was really in the right place at the right time because right at that point in time where the business world was, was starting to use information technology to help support rather than replace decision makers. And that's exactly what decision scientists are interested in.

00:02:59:03 - 00:03:18:16
Mark Silver
So, there's this play by Molière where the character is so excited to learn he was speaking prose all his life. He always wants to speak prose. So, it was the same thing. I discovered that what I was doing when I was programing for the university actually had a name. I was doing decision support systems, and now the theory is to go with it.

00:03:18:18 - 00:03:20:12
Mark Silver
And that's what I've been doing ever since.

00:03:20:13 - 00:03:40:18
Jie Ren
Nice. So, you got inspired by your professors, and then maybe because of that, you are motivated to become a professor yourself, right. And also, to talk about decision support and now there are so many different versions of description about it. Right? So, business intelligence, business analytics, data science, data analytics. Right.

00:03:40:19 - 00:04:03:11
Mark Silver
Yes. I think well, I don't want to say wasn't a lot of things about my professors, but I think I just love the universities. I like being on the campus. And so, it was just I was to spend my life in universities, and I like doing research and teaching. So that's when I became a professor. But for many of those years, I really was specializing, as you said, in decision support.

00:04:03:12 - 00:04:22:12
Mark Silver
But, analytics in many ways is new-fangled decision support. Yes. But what I really became more interested in was not the technology, but really how people use the technology, which is the behavioral side. So that's really what information systems is. It's the combination of technology and behavior.

00:04:22:14 - 00:04:44:00
Jie Ren
Nice. Since you are talking about the behavioral side right. Let's talk about the digital transformation. Right. Of so many different things: could be business, could be society, could be individual use of technology. We know that technology has been transforming society for years. And now the very hard work is AI, right? Yes. So, I've seen examples, such as for example, no.

00:04:44:00 - 00:05:01:15
Jie Ren
The internet. Right. Personal computers and then user generated content, social media, virtual reality, augmented reality, AI and recently personal AI agents. Could you please give a give me some examples about the digital transformation.

00:05:01:17 - 00:05:19:01
Mark Silver
Certainly. So, I kind of think of it as four phases. And the interesting question is, by the way, whether there will be a fifth phase or whether we're going to be in this fourth phase forever. But it started out in the 1950s, 1960s. We'll give you too much just because of the 1950s and 1960s with really automating.

00:05:19:06 - 00:05:43:20
Mark Silver
They were called transaction processing systems, where basically companies save money by replacing people with machines to do their accounting and that kind of thing. But what would happen was because they were doing this, they were accumulating mountains of data, and they just sat there. And so, people said, why don't we reprocess this data, give it to managers so they can manage better?

00:05:43:22 - 00:06:14:09
Mark Silver
And that's where we got management information systems. And what I was referring to is decision support systems. And that was like the 70s and the 80s. It was still doing the other stuff. And now we're doing something else in the 90s– late 80s-90s, knowing these businesses here and there started figuring out that they could use technology strategically. So, if you think about airlines like American and United that had the best reservation systems, they did all sorts of things to get strategic advantage from them.

00:06:14:11 - 00:06:14:22
Mark Silver
And, you know, a lot–.

00:06:14:22 - 00:06:16:18
Jie Ren
Lot of the data in it, right.

00:06:16:19 - 00:06:35:13
Mark Silver
Because the data and also because of the way they manipulated the functions, the applications, and because of the way they related to their competitors, also because they had the best systems, their competitors had to use their systems. So, if you think about it, a lot of the competitors are out of business. American and United are still here.

00:06:35:15 - 00:07:04:00
Mark Silver
There's a company called American Hospital Supply. They've been bought by someone like somebody. So, they don't exist anymore. This is their so, like, so simple to you. But in 1980, this was a big deal. They put a terminal in a hospital so hospitals could order supplies electronically. And because hospitals order hundreds of thousands about all sizes of Band-Aids on supplies, and they can't run out because you can't tell a patient, we are don’t have any oxygen today.

00:07:04:01 - 00:07:29:17
Mark Silver
So being able to order electronically gave the hospital some of the benefits. The American Hospital Supply got a 50% market share. One more example, which would be oh, let's choose. Oh, Otis elevator. So, Otis Elevator was already the leading elevator company, but they put microchips–They put microprocessors in their elevators to create what we take for granted now as smart elevators.

00:07:29:19 - 00:07:54:10
Mark Silver
But because they own so much of the market by creating smart elevators, they weren't only selling elevators, they were the only ones who could service their elevators. And gained a competitive advantage. So, to summarize, the strategic phase is the businesses found ways to get competitive advantage over their rivals. Now, you asked me about transformations and digital transformations.

00:07:54:12 - 00:08:36:12
Mark Silver
All of those are transformations. But what's really transformative is the phase we're in now and the technologies that you mentioned. And so classic examples: Amazon just transformed the way people buy things. And Netflix and others transform the way we're entertained. From physical media to streaming media. There's a company called Fresh Direct that transform supermarkets that instead of having lots of physical supermarkets, they just have one giant warehouse and they deliver to the customer from that warehouse, they get all sorts of economies of scale, the customers get all sorts of convenience, and it's a win-win.

00:08:36:14 - 00:08:55:00
Mark Silver
And they're they've transformed that industry. And of course, social media was– we know social media has radically transformed society. Yeah. And everybody says AI is going to, I think we all know that's true. We just don't know how. And that's a little frightening. But that's another topic for another time. Yeah.

00:08:55:02 - 00:09:25:18
Jie Ren
So, all the examples that you gave. Right. They are transforming the societies in a very, massive way. Right. You know, so these are the technologies that are staying, right, to transform the societies. Over the many years, we also see we've seen some technology that didn't get to stay. So, like, I sometimes tell my students, tech is very much like fashion.

00:09:25:19 - 00:09:48:02
Jie Ren
Some trends come, some trends go, and some trends choose to stay and become the norm. Right. So, the example that it gave up on social media, everybody's using social media right now. And we are now so excited about it because that's the norm. Right. So, in this whole process, long process, of many waves of technological advancement. And then in your opinion, what is the role of the users?

00:09:48:04 - 00:10:15:05
Mark Silver
So, the role of users, well, let me tell you, someone else's opinion. First, you often hear people talk about the impact of technology on people, businesses, society, and kind of the images that there's a highway, a person or business or all of us are standing on the highway and a truck is coming down the highway 100mph, and it slams into us.

00:10:15:07 - 00:10:17:08
Jie Ren
And I say.

00:10:17:10 - 00:10:55:00
Mark Silver
Exactly whatever happens to us is the impact of technology. But that kind of implies that we don't matter. And it's really what you might call a kind of a focus on the technology of doing things. But that's just not true. We play a role in how we adopt, and, as you said, how we use technology. And so that I think is a better way to think about it, is not the impact metaphor, but it's really that there is an interaction between people and systems, and it's that interaction that leads to good things.

00:10:55:00 - 00:10:56:11
Jie Ren
Is the behavioral aspect.

00:10:56:11 - 00:11:15:20
Mark Silver
That it's the it's the behavioral aspect. Here's just a simple example that I love. And by the way, that list metaphor I gave you was from a professor, a late professor Robert Kling. So, I want to give credit where credit is due. I can't remember the name of the person who wrote this dissertation, but it's a great dissertation.

00:11:15:20 - 00:11:31:15
Mark Silver
Maybe 20 years ago, he was studying in E.R., in an emergency room in a hospital in Boston. And it turned out that almost all of the patients were from– You want to guess? You'll never guess.

00:11:31:17 - 00:11:33:20
Jie Ren
Not Really. Tell me.

00:11:33:20 - 00:11:48:20
Mark Silver
Afghanistan. Now, why were all the patients from Afghanistan? Well, someone was analyzing the data and says, ‘you got to figure this out’. Well, it turns out that if you take all the countries and listed alphabetically, Afghanistan is the first one.

00:11:48:21 - 00:11:49:14
Jie Ren
Oh.

00:11:49:16 - 00:12:05:19
Mark Silver
And they made the interface alphabetical order. And so, this is the E.R. The nurses, the technicians, the doctors, they trying to save someone's life. They don't care where they came from. They just picked the first thing on the list all the time. So, you got bad data in the system.

00:12:06:01 - 00:12:07:16
Jie Ren
That's human error. Yeah.

00:12:07:18 - 00:12:27:16
Mark Silver
And that's human error. But that's not an exception. The very high percentage of the data in our systems is wrong because of human error. But I will say a little differently. It's not just human error because the human made the mistake because the system was alphabetical, but it's not the system's fault because the people could have picked the right thing.

00:12:27:20 - 00:12:45:05
Mark Silver
It's that interaction between the two. And that's in so many cases what really matters. I'm gonna add this in some cases, what people don't think about because they think about one or the other. The technology made me do what the computer made me do it, or I'm sovereign, I do whatever I want with my technology.

00:12:45:07 - 00:13:06:05
Jie Ren
So, I kind of understand it. Like as a technology provider, as are developing the technology. So it could be, you know, any versions of the software. You also need to really put yourself in the shoes of the users and consider that context. Consider this user experience and then you know, revisit your design.

00:13:06:07 - 00:13:16:17
Mark Silver
That's exactly right. And I think most of the major companies have finally figured that out. But even so, very often it just doesn't happen.

00:13:16:19 - 00:13:37:22
Jie Ren
Okay. So, let's revisit, right, the educational aspect of our conversation in terms of your teaching style. Right. Because you mentioned that you love the campus environment and you want to be a professor, from many years ago. So, what is the most important thing that you teach to your students about tech?

00:13:38:00 - 00:14:02:01
Mark Silver
So, the very first class, I ask him a question and I say, “What do you think is going to give you the most important thing in this course? But tell me in a single word.” And so, they used to say computers, and computers are there, but that's not the most important thing today. So even computers. Right. It's an iPhone.

00:14:02:03 - 00:14:21:10
Mark Silver
It’s whatever. So then sometimes they say information and I like that a lot better because information isn’t about the technology. It’s about what you do with the technology. Apply that. You create information. You process information, you use information. But it’s still not the answer I want. And I tell them the answer. It really surprises them. The answer is consequences.

00:14:21:12 - 00:14:21:20
Jie Ren
Okay.

00:14:22:01 - 00:14:46:06
Mark Silver
I would go a step further and I would say consequences is one of the most important concepts in life. Think about what parents tell their children. If you don't behave, there will be consequences. Exactly. And this child thinks that consequences mean the same thing as punishment, but it doesn’t. There are two different things. If you tell a–.

00:14:46:08 - 00:14:47:13
Jie Ren
Positive consequences.

00:14:47:17 - 00:14:48:01
Mark Silver
Exactly.

00:14:48:02 - 00:14:51:06
Jie Ren
If you keep eating your vegetables, you’ll get an award.

00:14:51:07 - 00:15:12:21
Mark Silver
That’s exactly right. And it's like that's really important that there are positive or negative consequences. And punishments also aren't the same even as negative consequences. If you tell a child, if you run with that ice cream cone, you're going to drop it and they drop it and goes splat. You didn't punish them. It was a natural consequence. And so, this is what so where do the consequences come from?

00:15:12:23 - 00:15:38:22
Mark Silver
It came from the child's behavior, their actions. And so that's really the most important thing for my students to know that actions matter. The actions lead to consequences, and consequences are all what it's all about. Let me give you, if you like, an example. So, I'm going to go back to the Afghani’s, in the emergency room or the people who aren't really Afghani’s in the emergency room.

00:15:39:00 - 00:16:04:12
Mark Silver
So, you might say, well, that's a silly example. So, you get bad data. But what's really hot today? Business analytics, data analytics and AI. And what do they drive off of: data. So, if your data are bad then your analyzes are bad. You know, the recommendations you get are bad, the decisions you make are bad, you or your business or society are in huge trouble.

00:16:04:14 - 00:16:22:07
Mark Silver
So, AI analytics only work if you have good data, and the consequences are what you do with the data. But it still matters about the data because the consequences, if you don't think about the consequences, you won't know how to create the good data.

00:16:22:09 - 00:17:01:19
Jie Ren
Yes, that's very true. It feels like there are two dimensions based upon what you said. One is on the individual level and then turns out that, many individual users, including myself, right. We are at the very vulnerable side. We are very much like a pie safe watching the trend coming. Yeah, we don't have much say in it unless we advocate for our users’ rights very much through social media movements, etc. to let our voices out or give feedback to the software vendors, or to push the managerial people to say, hey, this system doesn't work, we have to get a new one.

00:17:01:21 - 00:17:26:12
Jie Ren
So, this is one part and then the other dimension is from the societal level. So, what are the consequences of using tech, to affect the society? I'm sure there are like positive sides and also a negative side. You said something about in the very beginning, right? When certain technologies are being introduced to the workplace and then there could be impact on the labor market, right?

00:17:26:16 - 00:17:35:17
Jie Ren
So generally speaking, we don't need to talk about a labor market per se. But general speaking, what is the consequences of using tech to society?

00:17:35:19 - 00:18:01:06
Mark Silver
Let me give you first an indirect answer, kind of a lead up, and they'll give you a direct answer to your question. So, we were talking about the interaction between technology and people or businesses. So, if you think of the examples I gave you before of just successful strategic use of technology, Amazon, Netflix Fresh Direct, those people didn't throw technology at the problem.

00:18:01:08 - 00:18:10:02
Mark Silver
They had a fit between their business and their business model and the technology. And that's really why they succeeded.

00:18:10:04 - 00:18:12:00
Jie Ren
The alignment.

00:18:12:02 - 00:18:30:02
Mark Silver
The alignment. And there are lots of businesses who don't do that or didn't do that. They just focused on the gee whiz technology, and they went out of business. And that was part of what the dot-com bust was. And so, it really is the interaction between these five forces that leads to consequences.

00:18:30:05 - 00:18:42:06
Jie Ren
And also, very much like the AI. The AI face that we are in, very many companies are somehow proactively or passively adopting AI because their competitors are doing so. This is becoming the industry standard.

00:18:42:12 - 00:18:43:01
Mark Silver
Exactly.

00:18:43:01 - 00:18:44:23
Jie Ren
A lot of confusion that could be happening.

00:18:45:02 - 00:19:04:18
Mark Silver
That's why that happens in every year that some companies are using technology strategically and some are using it, out of competitive necessity. But somewhere, somewhere in between, they don't really know why they're using it. And as we get into trouble. But, you know, let's use AI as the example so we, we know or at least we believe. I guess we know that there is an upside

00:19:04:19 - 00:19:27:01
Mark Silver
to AI, at least were promised one that will be more efficient, will be more effective. And then what does that mean? We'll be we'll be richer. We'll have more leisure time where your life will be wonderful. But we also know already that there's a downside to AI. I mean, the first is the same downside we have with so many technologies that if you don't do it right, you don't get the benefits.

00:19:27:03 - 00:19:53:22
Mark Silver
There was a study out of, I think, MIT recently that 95% of pilots and businesses with AI have actually failed. So, part of it is that. But the other part of it is actually when you succeed in some respects, maybe you're more efficient, maybe you were more effective, but you have all these other negative side effects, maybe for you, maybe for your employees, maybe for society.

00:19:54:00 - 00:20:23:11
Mark Silver
So, think about it. And this is all over the news that what it's almost like back in the 50s and 60s with transaction processing, that AI systems are replacing people. Now, the answers I hear is that: no AI systems will, in the long run, augment people, and so people are going to lose jobs. That's ridiculous. You may have already people already losing jobs and more people are going to lose jobs.

00:20:23:14 - 00:20:27:06
Mark Silver
So, unemployment is an issue. But now let's think about -

00:20:27:09 - 00:20:38:07
Jie Ren
AI is also creating new jobs. Right? So, we need to like reskill right? And also adjust our curriculum etc. in order to help people to transition.

00:20:38:09 - 00:20:59:09
Mark Silver
Well, let's do so. This is now my personal opinion. And a colleague of mine who probably knows more than I do. You said, don't say this unless you disagree. He just said, don't say this. But throughout history, throughout those last 75 years, I was describing to you. People have always feared that new technologies would lead to massive unemployment.

00:20:59:11 - 00:21:15:13
Mark Silver
And what happened was some people were unemployed. But technological advances always led to economic advances and overall, more common good and more employment. I'm not sure that's going to be the case with AI. That's, that's what he told me not to say.

00:21:15:15 - 00:21:21:16
Jie Ren
It changing too fast. And then the speed for people to reskill, right, is not matching the speed.

00:21:21:18 - 00:21:23:03
Mark Silver
Thus, if there's a big part of it.

00:21:23:06 - 00:21:23:17
Jie Ren
Of Developing AI.

00:21:23:17 - 00:21:44:03
Mark Silver
And because it's also moving so fast, we don't really know where it's going. But, you know, well, people used to say with those systems that just replaced clerks with physical processing as well, knowledge workers aren’t going to be replaced and people who do physical jobs aren't going to be replaced. It's just people who are pushing paper. But now knowledge workers are being replaced.

00:21:44:05 - 00:21:52:23
Mark Silver
Accountants, lawyers. We have robots replacing physical workers. The robot could clean the bathroom for you. So, I really do worry about employment.

00:21:53:01 - 00:21:55:23
Jie Ren
So, in order to mass produce this. Right.

00:21:56:00 - 00:22:16:06
Mark Silver
Exactly. So, I really do worry about- not to scare everyone, but I worry about the societal effects. But it gets even worse. I'm getting there. Up there. Oh, upsides, don't get me wrong. Yeah, but the feeling if there's any less message, I want to send to you and your listeners is that if you don't worry about the downsides, they're going to get you.

00:22:16:08 - 00:22:54:00
Mark Silver
If you worry about them, then maybe you can find a way to avoid them or make things better, as you were saying, purposefully reskilling. So, here's how it gets worse is what about the people who don't lose their jobs? The people who really are still experts that are needed for their human expertise and their experience? Well, how do they get expertise and experience if they are being computer augmented, if they are relying on the computers, if they're relying on AI. What I think is ridiculous, it’s just my opinion, is people say you can't trust what comes out of ChatGPT.

00:22:54:02 - 00:23:15:00
Mark Silver
So, you got to check now. Yeah, but I was thinking it's slightly different for now. And you wait every week or every month, the hallucinations get better. Yeah, but they say you've got to check it. But for now. So, if it's a factual thing, you could check and say, well, that doesn't exist. Even though it told me it does. The story, isn't there.

00:23:15:02 - 00:23:41:06
Mark Silver
But what about when it makes recommendations? It's giving you advice and suggesting how to make decisions. There's no right or wrong up front. You have no way of checking whether it's right or wrong. All you can do is ask an expert. But if you are supposed to be the expert, you've got nobody to ask. So, I think a second real concern is going to be the reskilling of the whole workforce, even people who are unemployed.

00:23:41:08 - 00:24:06:23
Mark Silver
And that's not a good thing. Then we have and actually your research touches on this a lot, which is biases. Yeah. The biases that come out of AI system for a variety of way are known. And this is bad. Hear we go again with Bad; for at least two reasons. One is if you're relying on the information to make decisions, biased outputs lead to biased decisions, which are usually bad decisions.

00:24:06:23 - 00:24:31:16
Jie Ren
In a sense like these algorithms can be used to make decisions, right. So, it's autonomous and everything. So, and then if the trust is not there per se. Right. And then people okay, let me rephrase it. So, if people trust it too much, the decision makers, without knowing there could be biases and everything and there will be consequences then

00:24:31:17 - 00:24:55:17
Jie Ren
Right. So, some people could not get the job because of certain hints that are reflected in the resume, that are not the good candidate, you know, like material so that their qualification is never evaluated before making a decision. Right. So yes. So, we definitely will need, you know, more attention towards this AI biases for sure.

00:24:55:18 - 00:25:16:09
Mark Silver
So that you're really saying is there two effects of the biases. One is just bad decision making but the other is discrimination. And this is really what your research focuses on as I understand that. That people will be discriminated against by those biases, that it will advantage some people and disadvantage others. That's also true today of social media.

00:25:16:09 - 00:25:19:15
Mark Silver
Yeah, but AI just makes that all worse.

00:25:19:17 - 00:25:22:17
Jie Ren
So, it is a black box right? Too many people.

00:25:22:17 - 00:25:47:19
Mark Silver
Exactly. But as you're talking about trust and replacing people now here's the conundrum. This is the classic question. You're putting a regulatory agency. Let's say you're responsible for a nuclear power plant. What do you want to have happen when the alarm bells are going off? Do you want to use AI to figure out whether they should shut down the system automatically?

00:25:47:21 - 00:26:08:21
Mark Silver
Or do you want a human being to do it now? The AI didn't get a bad night's sleep. It wasn't stuck in traffic. It didn't have a fight with a significant other. It's going to be much more consistent. So, you might say, hey, slam dunk, let the AI do it. But then thinking about what we were saying a little while back, the AI doesn't have any experience with this decision.

00:26:08:21 - 00:26:31:18
Mark Silver
Yeah. It doesn't have the human factors to take into account of, well, this is the high consequence event. If, you know, heaven forbid, radiation spreads, maybe you really do need a human being with human sensibilities, sensitivities, making a decision. And it's a concern. And I agree with you completely. Over time the balance is going to shift but it can’t shift all the way.

00:26:31:20 - 00:26:48:23
Mark Silver
And so, it's a problem. So yes, another issue are those biases and their decision making. But there are other things too. For example, all of this is driven by data. Just like– and this is true social media also it raises lots of privacy issues.

00:26:48:23 - 00:26:52:03
Jie Ren
Yeah. Yes. Copyright issues too.

00:26:52:05 - 00:27:18:03
Mark Silver
You actually right. From two really separate issues with that way. There's intellectual property and intellectual property issues in terms of, of copyright. And there's just privacy. And so, these are again, negative consequences. How could we deal with them? Well, companies could self-regulate, government could regulate, which they may well do what they've done in some cases more in Europe than in the United States.

00:27:18:05 - 00:27:19:07
Jie Ren
Yeah, exactly.

00:27:19:09 - 00:27:43:02
Mark Silver
Individuals can do what they can try with to do or business to protect the privacy by not letting some of their data out and using internal systems. But it's ultimately a real challenge. And so, it's, again, one more potential downside, which doesn't mean we shouldn't do it. But again, the message is if you don't think about the downsides, they are definitely going to get you.

00:27:43:04 - 00:27:48:03
Mark Silver
So, the answer is to plan for them and try to minimize those downsides.

00:27:48:05 - 00:28:27:23
Jie Ren
Okay. It sounds like for these societal level consequences. And then there could be even more messy because it's affecting every single person. In order to address that, we for the individual level effort is not enough. So, we need a collaborative effort from a few stakeholders, the regulators for sure, and different tech companies for sure altogether and also individual users, advocating for our rights and then at the same time to take preventative measures in order to, you know, protect our data, for example, data privacy, etc., and then to use the technology properly.

00:28:28:01 - 00:28:45:04
Mark Silver
Let's just for a second there, you gave me what will either be a great idea or a terrible idea, which would be the to crowdsource the solution to these problems. But I'm not sure that’s sort of like using technology for the whole thing. I'm not sure that that would actually have a good outcome, but that's really an interesting thought.

00:28:45:06 - 00:29:19:16
Mark Silver
But yeah, but the question you really asking about how do we do these protections, I think this is an open question that– but again, there are lots of people who are asking the question and they're the ones who are really going to get most burned by these privacy issues. And so that's why I, you know, I use the word consequences a lot, but you have to think about not just what you're doing and whether you're enjoying doing it, and not just with the positive consequences you're aiming for, which you may fail to get.

00:29:19:18 - 00:29:39:22
Mark Silver
We really do have to think about what could be problematic. Let's go back to most airlines with reservation systems. It turns out some of the things they did to get a competitive advantage were actually illegal. Well, and some of the others were probably unethical. So again, just because you can get benefits doesn't. And the government stepped in to prevent that.

00:29:39:23 - 00:29:49:22
Mark Silver
So, we've there's a history of government playing a role in, in using policy to protect individuals and businesses.

00:29:50:00 - 00:30:15:04
Jie Ren
So, you touched upon two important, points here. One, we can continue to talk about the different categories of consequences. Right. And then the second one is what should we do about it? You talk about like users, right. Preventative measures to protect the data and everything. So, let's first continue to talk about the categories of the consequences.

00:30:15:06 - 00:30:36:07
Jie Ren
So far, we have mentioned individual level and societal level. More about the misuse of the system. You know why. But do you think that we are relying on technology a little bit too much so that we are losing the ability to think, on our own, for example, the use of GPS, right. So, we want to go, oh–

00:30:36:08 - 00:30:39:10
Mark Silver
Yeah, I was oh, sure. I was going to say and I also.

00:30:39:10 - 00:30:40:12
Jie Ren
Yes, and AI.

00:30:40:13 - 00:31:01:15
Mark Silver
It depends a lot on the individual. You know, the some of us are and some of us aren't. But you're right. GPS is always a great example. And let me give you two different ways with GPS examples. Yeah. The first is just a plain old reliance. Well, is a different question, which is why do we use them at all?

00:31:01:17 - 00:31:25:12
Mark Silver
We use them to get where we're going as efficiently as possible. But what we do. So, Waze tells us leave at 10:30, so we leave at 10:30. Maybe if we're conscious, we leave at 10:25. Now, if we didn't have Waze, if we were in somebody's directions or a map, we'd probably leave at 10:00 because we know we're going to make a wrong turn somewhere.

00:31:25:14 - 00:31:40:17
Mark Silver
So, we rely on Waze. And then there's a traffic jam Waze didn't spot in time. It’s not Waze’s fault. It just isn't capable of spotting it in time or our phone dies because the battery runs out. And now here you.

00:31:40:17 - 00:31:42:09
Jie Ren
That happens to me a lot.

00:31:42:11 - 00:32:03:08
Mark Silver
Exactly. I was thinking that you are two miles per destination and you have no idea how to get there. So sometimes we fail to accomplish our objectives because we're so reliant on technology. We don't do smart things to protect ourselves. But even more extreme case is where we just kind of blindly use the technology and dumb ourselves down.

00:32:03:08 - 00:32:04:06
Jie Ren
Then over trusting.

00:32:04:06 - 00:32:28:06
Mark Silver
Over trusting them there. And so, the example there's there was an episode of The Office where they're driving. They drive into a lake because they're just following the GPS. It's funny. Yeah, and you might say it's funny and it's a sitcom. And, you know, these things don't happen in real life, but they do happen in real life, in fact.

00:32:28:06 - 00:32:30:18
Mark Silver
And this is hard for me to believe.

00:32:30:20 - 00:32:34:17
Jie Ren
Maybe not to the lake per se, because they're too obvious, but dead end for.

00:32:34:17 - 00:32:56:11
Mark Silver
Sure. But the this is the not the lake. How about the ocean? Check this out because I didn't believe it when I read it at first. There were there was a couple in Australia. They were tourists. They drove into the Pacific Ocean because the GPS told them so. So, if you're asking, do people get them down by technology.

00:32:56:13 - 00:33:00:00
Jie Ren
I wonder, would that conversation could be in the car.

00:33:00:02 - 00:33:00:08
Mark Silver
When they–.

00:33:00:08 - 00:33:03:20
Jie Ren
Are seeing the ocean and then doubting that use of technology, then.

00:33:03:23 - 00:33:23:14
Mark Silver
Exactly. But let's think about it though. Our cars have all sorts of safety features built in, I'm not sure it's going to protect against that drowning though, although there are devices you can use to smash the windows too. But that's really part of the answer to the question you've been asking all through is, what can we do? We can be proactive and we're not.

00:33:23:16 - 00:33:44:13
Mark Silver
Maybe that's an unfair generalization, but I don't think we are. I think we all know what I've been saying, which is technology can blind you and we know it because we've been burned and we've been burned more than once. But like a week later, we forget that we were burned and we get overly– we can drive in the Pacific Ocean, as it were.

00:33:44:19 - 00:34:10:10
Mark Silver
We don't pay attention to consequences, and we let it happen. And so that's why you really have to proactively think about two things. What might prevent me from succeeding. And even if I succeed, what might go wrong. So, think– this put it back in the business context. So, lots of businesses use systems to try to increase sales.

00:34:10:12 - 00:34:36:14
Mark Silver
Lots of businesses use systems to try to decrease costs, and sometimes they succeed and sometimes they just don't because they didn't understand well enough their market or their customers. So, it's not where we say it is. The benefits aren't automatic. You have to work for them. You have to make sure you understand how you're going to increase sales with this CRM system.

00:34:36:16 - 00:34:57:13
Mark Silver
Similarly. Well, let's say you're trying to decrease costs. Well, what if you succeed in decreasing costs, but you really degraded the quality of your product or the quality of your customer service. So now you've got a win here and a loss here. And how do you balance. Now for some businesses it's a net gain.

00:34:57:18 - 00:35:26:07
Mark Silver
For some it's a net loss. So, the important thing is it's kind of like medication, that we take medication because we need it or because it hopefully will do good things for us. But every medication has a warning label on the bottle. Yeah, exactly. It says it may cause all these side effects, and you and your medical professional have to decide, is it worth the risk of the side effects to get the potential benefits?

00:35:26:09 - 00:35:31:10
Mark Silver
But notice how I said it. The risk of the side effects and the potential benefits.

00:35:31:10 - 00:35:32:06
Jie Ren
It's not guaranteed.

00:35:32:08 - 00:35:45:20
Mark Silver
Neither one is guaranteed because we live in an uncertain world, and now we get a little more certainty when we do government policies and so forth. As you said, although that raises its own its own concerns.

00:35:45:22 - 00:35:55:00
Jie Ren
I really like the metaphor that I gave about the medication. So, by the potential guess there's also the side effect. So, we hope.

00:35:55:01 - 00:35:57:03
Mark Silver
To give you just another example from our industry.

00:35:57:03 - 00:35:57:12
Jie Ren
Yes, please.

00:35:57:14 - 00:36:21:15
Mark Silver
Which is education. Just digital notes. So, there are a lot of professors who will now give the students their notes in advance, or they'll tell the students you will get a recording after class. And the theory is that you're making life easier for the student. They can pay better attention in class because you're giving them the notes that are working.

00:36:21:15 - 00:36:44:22
Mark Silver
They're going to get better notes because you are giving them the notes. But what studies show is that attendance at these classes dropped significantly. And so, you know, a lot of professors won't give out the notes. And so, it's again, there's an upside and there’s a downside. But you know, they determine whether the attendance drops significantly: It's how interesting the professor is.

00:36:45:00 - 00:37:08:06
Mark Silver
If you're one of these swinging from the chandelier’s great professors. They'll still come to your class even if you give out the notes. If you are putting people to sleep, I'll sleep at home. So, it's the giving interaction is not the technology. It's not the professor. It's some combination of the professor, the students, and the technology. But these will lead to better education or worse education.

00:37:08:08 - 00:37:34:18
Jie Ren
So, I like the two examples that you gave. One about the medication that is more certain than the educational setting. So, in the very in the more certain scenario, you are presented with the potential gains and also the possible side effects. So, kind of know what could happen in terms of the consequences. And then you can make a more cautious decision as a user if you pay attention to this type of information.

00:37:34:19 - 00:38:07:09
Jie Ren
The second one is more uncertain. Out of good intention though, like sharing the notes and everything. So, from the educator's point of view or from the student's point of view, there are more uncertainty out there. So, they don't know how to react to this until that happens. Right. So, it's kind of like a learning curve, right? So how do users foresee these consequences, possible consequences, so that they can take certain, you know, actions or decisions in terms of adopting this technology in certain ways?

00:38:07:11 - 00:38:35:17
Mark Silver
So, I think that's true. And I think it harkens back to a distinction made before that was really important between individual users and business users. So, let's talk first about the way you framed it for individual users. It's actually a hard question, but that's part of what technology education needs to be about. When people self-learn tools, I mean, everybody knows how to post a pic because they learned that, but they don't know when not to post a picture.

00:38:35:19 - 00:39:04:22
Mark Silver
I agree with that. It can come back to haunt them. And so ultimately you can't know in advance all the possibilities and know what decision to make. But I really think we can educate people or educate ourselves when we have the materials. So, understanding the connection between what we do, and the technology, and what is going to happen, and by doing that, we'll be in a position to do that.

00:39:04:22 - 00:39:20:02
Mark Silver
I mean, again, people don't always learn. A week later we fall back to that their habits. So, it's really, again, I think the role, the role of educational systems to train people. There is another answer, which is AI.

00:39:20:04 - 00:39:24:17
Jie Ren
Yeah, yeah, a lot of uncertainty. So, we are learning as well as educators.

00:39:24:18 - 00:39:46:16
Mark Silver
Well so but you know AI ways is potential upside and there’s a potential downside. There may be the tool to warn people, that when they're using some other tool maybe the AI warns you don't rely so much on Waze. And so that would be an interesting, how for individuals. But for businesses, businesses should know better. The businesses do risk analysis on large projects.

00:39:46:18 - 00:39:47:00
Mark Silver
Yeah.

00:39:47:01 - 00:39:48:16
Jie Ren
I mean if you have owned divisions.

00:39:48:18 - 00:40:09:16
Mark Silver
So, in theory they should be able to do it, but they're too they may not be insightful enough about the possible set of negative consequences. It's almost as though you have to do a brainstorming exercise to ask yourself what could go wrong, and then what are the probabilities? But the probabilities don't matter until you first figure out what could go wrong.

00:40:09:18 - 00:40:30:03
Mark Silver
For me, this is an oldie but goodie. This goes back a lot of years. Yeah, well, Bank of America was at one time the technological leader among banks worldwide. They invented the magnetic ink. That's on everybody's checks. They invented all sorts of other things. But then they were putting in place a new system to manage their trust division.

00:40:30:05 - 00:40:59:02
Mark Silver
And I think I may get the numbers wrong, but I think it was a $20 million project that came in several years late for $80 million. But it never really worked. Right. But that's not the worst of it. It was managing their trust division. They didn't have a system that worked. And so, all of the trust clients with the somebody with the system that did work, and lost billions of dollars and the mistake, well, there are all sorts of things that can go wrong, you know.

00:40:59:04 - 00:41:20:18
Mark Silver
But the mistake was at the beginning. They didn't ask themselves what would go wrong and protect themselves against that. So maybe are we took too much risk? The scale was too big. We were just trying to fix something, and instead we tried to reinvent it and we invented this poorly. So, businesses fall for that trip also. But one would hope that it would be less likely.

00:41:20:20 - 00:41:35:12
Jie Ren
You touched upon the answer to my next question. Okay, okay. Which will be what are the actionable implications, for individuals or societies considering the consequences?

00:41:35:13 - 00:42:00:01
Mark Silver
Great question. It's on the one hand. It's the most important question. And yet I always feel uncomfortable trying to answer it because I'm afraid my answer isn't good enough or seems obvious or intuitive, but the real reason the consequences matter. The reason that it matters to parents and children, the reason it matters to businesses using technology is because our actions have consequences.

00:42:00:05 - 00:42:22:19
Mark Silver
And you asked me that was actionable. And so, the answer is you need to act wisely. And it's not clear if you could teach someone to act wisely. It's not clear if you tell yourself to act wisely. The first step, though is to tell yourself to act wisely. The second step is to be proactive. And so, what's really is actionable is kind of the things that you said.

00:42:22:19 - 00:42:42:01
Mark Silver
Right? I hinted at that earlier. Is to ask yourself what could get in the way of more things going right and what could go wrong? The way I like to describe it to my students is you have three goals with technology, and this is all about acting wisely. You want to leverage the benefits. You want to maximize the benefits.

00:42:42:03 - 00:43:07:18
Mark Silver
You want to avoid the pitfalls. So, you know, avoid the things that are going to be disasters. And then you want to overcome the obstacles that may make the technology less useful than what it would have been or may give you that balance between some good and some bad. So, it's really being conscious of benefits, pitfalls and obstacles. And again, we could teach some of that, but a lot of it is just proactivity.

00:43:07:20 - 00:43:20:04
Mark Silver
And if I figure out a better answer to that, I'll be happy to come back on your podcast. I give you another answer, but I've been thinking about this for a long time, and I think awareness, is– just like with a lot of things, is really important.

00:43:20:06 - 00:43:48:13
Jie Ren
I totally agree with you. Especially right now, there could be a lot of confusion in terms of the use of AI. Everybody knows that we have to use AI because it's becoming the work norm, how you work. Right. But how to use AI wisely, right. That's a big question. So, kind of summarize our conversation, you know, you know, in the metaphor, which is it seems like technology itself is a double-edged sword.

00:43:48:15 - 00:44:18:20
Jie Ren
So, it has the bright side and also the concerning side. So, in order to fully embrace the bright side, we need to keep innovating about technology, right. To embrace the bright side. But for the concerning side, we do need, first of all, individuals’ efforts about being aware of this or taking the next step, which is advocating for our user rights and also technology companies’ efforts in terms of making responsible decisions.

00:44:18:22 - 00:44:36:21
Jie Ren
And then the last one will be the educators as well as the regulators efforts in order to, make people be aware of this. Right. And then also to address and of these tech risks.

00:44:36:23 - 00:44:37:20
Mark Silver
Exactly.

00:44:37:22 - 00:44:38:05
Jie Ren
Right.

00:44:38:11 - 00:44:39:06
Mark Silver
Very well said.

00:44:39:07 - 00:44:43:19
Jie Ren
Thank you. Thank you so much, Mark, for coming to my podcast. I really enjoyed our conversation.

00:44:44:00 - 00:44:45:21
Mark Silver
Thank you, Jie, thank you for having me. This is fun.

00:44:45:22 - 00:44:47:06
Jie Ren
Yeah it is. Thank you.