Ethics in the Age of AI

Daniel, great to be here, Jared. Good to see you. Good to talk to you.

For sure thing, Daniel. I'll start with, you know, don't feel bad that you didn't become a JAG. I wanted to be an f fourteen pilot and didn't have the wits to do that either. So, you know, when I was in college, I thought that I would go to law school.

I wasn't really sure what I would do with that. And, unfortunately, the director of admissions at the school I went to Wabash College was a former FBI agent and said, what are you gonna do with your law degree? And I said, I'm I'm not really sure. You know, maybe politics.

I'm not I just don't know. And and he said, well, you if you ever thought about the FBI.

And I had no idea that that was even a possibility. I thought that, someone came up at some point in your career and tapped you on the shoulder and, you know, gave you a secret handshake and you became an FBI agent. I didn't know it was something you could work towards. So I basically decided that I would start working toward that goal.

And as I got out of college and went to law school, thought about going straight into the FBI, realized that's a really difficult thing to do. The average age of an an agent is about thirty years old when they're usually second career. And had the opportunity, I saw a Marine Corps JAG team coming into a moot court and saw that and knew that my dad had served in the Navy. My grandfather had been in the Coast Guard.

I knew I wanted to serve, and I thought this is something I'm very interested in doing. And I worked, both as a defense lawyer and then later as a prosecutor and then on a staff for an admiral and got to see what it was like to be in the courtroom from both sides of the bench and also got to have the opportunity to be on a staff and give advice and counsel to leaders on ethical conduct and standards of conduct and legal and operational issues and really decided I like doing that. I did that for five years and applied to the FBI and was accepted and left the Navy on a Saturday and showed up at Quantico the next day on a Sunday and started my new agents training.

And then I spent a little over seven years working as a special agent. I investigated organized crime, violent crime, and I worked, was on a SWAT team and did all what I call the young fun stuff. And and then in the second part of my career, I became the chief division counsel, in the FBI in the Indianapolis division. And that was also a really rewarding part of my career.

I really enjoyed providing legal advice and operational advice. I was also responsible for media. I was the chief ethics compliance officer or division ethics compliance officer, worked as a congressional liaison as well and worked on training and undercover operations. And so but then also thought at some point, you know, I'm I'm ready to retire and would like another career And I really enjoy ethics compliance and had the opportunity to come to Cook Group, Cook Medical.

And it is a an incredible company and, great culture. And, I love the challenge. I love what I'm doing. And not often can you say that, you know, the people in a profession are genuinely good people almost always, but that's the case when you look at ethics compliance professionals and the kinds of people you get to be around.

It's really just an honor, and I enjoy what I do. Can't tell you how much I love it.

Sure. So first, I'll start with saying that both the Navy and the FBI are really highly regulated fields, that lots and lots of rules, lots of, bureaucracy, lots of policies and a strong commitment to core values and integrity.

Both organizations are one hundred or or more years old and both have expectations of really high standards of conduct. I really liked was working in an in an area where I could communicate and try to foster that culture of commitment to values, commitment to policies and doing what's right and getting to accomplish a mission, but to do it in a way that is, reflective of the values you have. And that's very consistent with what E and C is all about. It's it's helping organizations accomplish their mission, but in a way that's consistent with their values.

I think, you know, working with standards of conduct, promoting values, thinking about policy and training and investigating misconduct and mitigating risk. Those are all things that are part of being a naval officer or JAG officer, but also part of being in the FBI as a Chief Division Counsel and Ethics Compliance Officer. Those are the same things I do today and I really, really enjoy that. I think when you talk about technology, you know the Navy.

This may date me a little bit. I was in the Navy after ships stopped having sales, but I was in the Navy before there was an Internet and the DOD had their own LAN and WAN local access and wide access networks. And I happened to work at a at a base where there was lots of tech, lots of use of tech. We had a computer PhD scientist and a team of what I called the IT seals who were just creating incredible uses of technology at the time.

And and seeing that and seeing the importance of technology and staying on the cutting edge was something that was imprinted on me at that early time. And then the bureau as well, as I said earlier, the transformation that the bureau underwent post nine eleven was really a transformation that modernized in many, many ways a law enforcement agency into, incorporating business practices, but also a lot of technology.

Sure. I would say that it was sort of a combination of two things. I think from an opportunity standpoint, I looked at what the FBI was doing in the area of creating division compliance counsel and really embracing the development of ethics and compliance as a profession and being really forward thinking, especially for a government agency.

We had a lot of training about ethics and compliance and we were early adopters, I would say in the FBI. I think that was one thing. And then individually, I find the work really rewarding. It's for one, if you're curious, if you're intellectually curious, if you like to promote values, to train, to investigate, to get to the root cause of problems.

Those are the kinds of things that you do. And I really enjoy that. And I knew that as I transitioned out of the FBI, I wasn't necessarily looking to be in security or or even specifically in investigations or or using that sort of law enforcement background. But the work that I was doing in ethics and compliance was something I really enjoyed.

Your note about subpoenas is really interesting because when you think about an investigation, and maybe I really didn't think about this until I started doing investigations in the private sector and you realize that you're not showing up with an FBI badge to talk to somebody. You're not showing up with subpoena power or a search warrant. You are trying to ask them, would you be so kind as to talk to me, and would you be so kind as to let me look at your cell phone? Those kinds of things.

So there are analogs from those investigations, but it's a different thing. I'd say, you know, from when you make the transition from law enforcement or national security into the private sector, there are a couple things I would say from my perspective. One of the things that I always assumed was that the private sector had everything all figured out and they they were, you know, from a business perspective, everything was running smoothly. I had worked at a, you know, in the government and the government was obviously lagging behind the private sector.

I'd be honest with you. There are a lot of ways, not just at my company, but other companies where, you know, in the FBI, we were more advanced with our policy management systems, with our ethics compliance programs, with our strategic planning, with our risk mitigation strategies, with, having a chief knowledge officer, a chief information officer, lots of things that private sector, and again, not just my company, but other companies had not yet mastered or had up and running. And so I was a little surprised by that. We're here to protect them.

We're here to help them. But you don't really realize you're not exposed to how many really talented, good people of high integrity and incredible talent that are out there working to make the world a better place. I was very happy to see that and experience that, and it really gave me a completely new perspective on on things. I think a much more realistic perspective on how many really good people there are, especially in the ethics compliance field.

And and in the private sector in general, that there are people who are so smart and so talented. I think those are probably the biggest things. I think it's been an interesting transition, and it's but it's been good.

People's perception of you is an interesting point that I guess, you know, I hadn't really considered. Right? Because people see you as that ex FBI guy, ex Navy guy, and, you know, as you're adapting to the corporate world, they're kind of adapting to have, you know, a former law enforcement person there as well.

So that's it's interesting for both sides.

Sure. So I think if we start at the very basics, we would say that technology is really it's a tool. It's something that some sort of advanced tool that's gonna help you do something. But you have to decide first and foremost, what is this something that I'm trying to do?

And I think I was really fortunate in the Navy to have received a lot of continuous improvement training. The Total Quality Leadership Program, I think I had over two forty hours of quality training. And that sort of continuous improvement mindset is something that, you know, from the very beginning you have to ask yourself, okay, let's map this process. Let's see where we're trying to get.

How do we get from A to B? And let's look at the process. Are there ways that we can make the process more efficient? That's continuous improvement.

How can we reduce waste? But the other thing we can do is say, how can we be more efficient in these steps in the process? And technology is what helps us get there. And I go back and I think of, you know, technology or data driven solutions.

And I think about, you know, I'm interested in statistics mostly because I like sports. I like baseball. I like basketball. I like football.

And those statistics I've always found interesting. I think I look at things like data. I have a watch on today and I won't say I'm obsessed with, but I love to know what's my body battery. What's my how do I sleep?

You know, like all those points of data are good because they help inform me. But I think it's really important first and foremost to ask what is it we're trying to accomplish?

What ways can technology help us accomplish that? How can we be more efficient? And technology isn't always the answer. But more often than not, it could help us be if we pick the right technology and we have the right data as an input, then it could help us get to the right answers.

So I would say that when I think about technology in general, if you think about the FBI, for instance, you know, the FBI has the FBI laboratory, and it's, you know, long and storied tradition is that it was not only trying to be more professional in law enforcement, but also rely on research and rely on technology.

And so it has a long history of that. In the FBI laboratory, for instance, in the area of forensics, there is so much technology and so much work, not just even outside of things we would normally think of things like digital evidence review, but all kinds of forensic advances that come out of science and technology.

But I would say probably the biggest thing I would say during my career is that I was able to watch, you know, at the beginning of my career, a very large case, a very large white collar case that involved hundreds and thousands of banker boxes and lots of brand new agents carrying those banker boxes and walking into a room and setting them down on a table and going page by page by page and looking for evidence. And you think about where we are now and the fact that we have digital evidence review where you can look at the metadata and you can, you know, from the most simple using Adobe find and replace tech tool to very, very sophisticated AI to do digital evidence review.

You see the evolution and the ability to consume and sift through enormous amounts, really impossible amounts of evidence, you know, and I see that even in the work that you know investigations we do internally, we're able to look at emails in a way and you know electronic communications in a way that you would never be able to do one at a time to give us information. So I also look at other technology, you know, things like, you know, ground penetrating radar and satellites and all kinds of other things that were used in cases that I worked on that, you know, facial recognition and biometrics and it goes on and on and on.

And you just think, you know, it's a lot different than what it used to be in those ways. But also an investigation is an investigation. It's about getting to the truth of the matter. And a lot of times that's gonna involve not just an evidence review, but also interviews and building rapport with with individuals involved in the investigation.

The first thing I would say is that it's our responsibility to understand no matter what the technology is. I think the example we would be talking about today is AI. It's our responsibility to understand what what is AI. Like, not just what the headlines say, but talk to people who are experts and really try to create an understanding and develop an understanding of what AI is capable of doing, what some of the risks of using it are.

I think that's the place we have to start. We have to know our business. We have to know what we're trying to do, what kind of business we work in, what risk we have, what data is important to us, how we collect that data, and then how we normally would let that inform us. But then we have to understand what risks there are.

So we take what the technology is, what data we have and what business we operate in. And then we ask ourselves, okay, well, what risks are being created by this? So that's a broad answer to what a chief ethics compliance officer needs to do. How will AI affect our company?

And what do we need to do to mitigate risk? But then there's also the within my team, how are we going to use E and C? Every function has a use case. And so the question becomes, how will we use it to do our work better?

How will we leverage that technology to be more efficient, to be more effective in our promotion of the values, promotion of the corporate culture, achievement of the mission, and mitigating risks. So it's having that broad based understanding of the technology, having an understanding of the business and the business risks that that are involved. But then also thinking about how can I at a team level, an ENC level, how can we do our work better and accomplish our mission? And then even I would say all the way down to the personal productivity part of it.

Like, you know, I've I've tried to and I'm working on going from taking written handwritten notes to moving to using digital format so that I can more easily have that AI technology use that and leverage that to be more productive. So I think it's thinking of it, you know, maybe in a in a funnel. How does it affect society? How does it affect our business and industry?

How does it affect my team? And then how does it affect me is how I've tried to look at it.

Right. I would start with saying is no matter what company there is, every company has data and every company has a mission. And the data is going to feed into making decisions about how we accomplish the mission. And so the data might change, but the use cases that are out there, regardless of what the industry is, well, regardless of what business line it is, we're still gonna have the same issues regarding do we have the right data in the right format?

How can we leverage AI? What kind of use cases are there? And so regardless of whether we're talking about cook aviation or we're talking about cook medical and medical device manufacturing and distribution, the real question is, what are we trying to accomplish?

What data helps us make decisions that inform our strategy and helps us make timely decisions with better data, better analysis?

And, and then what risks are involved and how do we mitigate those risks? There are different use cases based on different business lines or different industries. But in the end, it really boils down to knowing what data is important, knowing how to collect that data, knowing how to apply the AI technology, and think about the risks, and also I would add unintended consequences.

Yeah, we are, and everyone who's using AI is whether they know it or not. And so when you think about the ethical considerations, the things that come to my mind would be first and foremost, data privacy and just how are we gathering the data to begin with? Was this gathered for for this specific purpose? Was there consent?

Is this a proper use of the data? What limitations did the owner of that data put on on the use of the data? How are we retaining it? How are we protecting it from a security standpoint?

How will we share it? So data, data privacy, data security, those are all fundamental because we're talking about data. I think also we have to think about very specifically bias. The idea that again, I don't have a an IT background, but what I do know is that if you boil it down to AI, generative AI and large language models and machine learning in general.

If you boil it down into the simplest terms, it's a black box that gets fed food of some kind, and then there's an output of some kind. And if you wanna really dumb it down to my, my level, I would say junk in, junk out. And so what you have to be, I think, prepared to do is think about if we feed it the wrong things, this model is going to spit out the wrong answers. And those wrong answers could be the consequence of bias in the system or bias in the algorithms.

And you want to make sure that you're always having that human eye, human touch, and a real focus on trying to avoid unintentional bias or or intentional bias. So I think that's a big one. I mentioned unintended consequences.

A lot of times I think what we haven't talked a lot about is that even if you've, you know, you're using the right data and that you've removed any bias and you feel confident that what you're getting is good, you might make a decision that has unintended consequences. And so thinking through what you're doing, what decision is being made based on that AI, you could end up with unintended consequence. I think thinking that through, I think also there's a real danger if the data is not good that you overemphasize the AI's ability to give you the answer that you need, and you put too much confidence in that decision.

And we could be out, even if it's not something that is violative of data privacy or that has bias in it, it could just be giving us bad information.

And so it's only as good as the what goes in. And so making sure that we're thinking through and putting that human touch on it is really important as well.

I think those are some of the ethical considerations that I would think of.

One thing I would say is that when we're considering working with AI and how we go about the governance and use of AI in our companies, I think it has to be cross disciplinary. It has to be cross functional. We can't in a silo say, okay, here's the we've got the data scientists. We've got the folks from AI.

We're gonna make a decision. This is what we're going with. We can't let the people on the business side say this is what we need out of it. This is what you need to make it do.

We can't have just E and C saying, hey, these are some of the ethical considerations or compliance considerations, and we can't just have legal saying, no, don't do anything. I think it's it goes back to one of our E and C function mottos, which is it's the idea that we are the department of no as in knowledge, not the department of no as in you can't do it because that's the easy way easiest way to mitigate risk. So I think, you know, it's important that we get everybody at the table and listen to everyone's perspective and opinion and experience, and then find a way forward based upon the consideration of all of those different perspectives.

You know, fortunately, you know, I think in any company, the best GC, the best, you know, lawyers are ones that understand the risk, understand how to mitigate that risk, but also understand how to get things done. And that's what we're we're all as a team trying to bridge that gap between what needs to be done and what would be done in a way that could cause some harm.

Right. Well, I would start with it's not unlike other areas of, legislation or other efforts to enforce, whether it's anti bribery, anti corruption, data privacy, trade compliance, sustainability.

You have every or most governments in the world trying to figure out how to motivate people to do things the way that government wants them to do it. You also have the issues with is it a country requirement or is it an EU requirement? Is it a, you know, US federal requirement or is it each state putting their own spin on it? And so there's there's lots of complexity in just, you know, monitoring the regulatory landscape and trying to figure out how you do things in a way that's compliant with all of the different regimes.

I think when we look at enforcement in general, what I would say is that there's lots of changes over this last decade. I would say first and foremost, we mentioned earlier, digital review. The fact that you can use digital review and that things are fewer and fewer paper documents and things are digital. It just means that a lot more investigation can be done a lot faster and a lot more cost effectively and a lot more efficiently, which means those investigations get a lot easier. I would also add that it's not just the digital review, but it's also things like whistleblowing.

There's a lot more incentives for whistleblowers and an understanding that, the whistleblower will have inside knowledge and inside understanding that is critical in making a government's case. There's also a lot of international cooperation.

Governments have figured out that if they want to work together, especially in transnational crime, as they work together, cases can be made in a way that they couldn't be made fifteen years ago. I'd also add that there's a lot more scrutiny in the area of civil society.

So watchdog groups and individuals have access to information that they would have never have had access to before. And that kind of transparency, that kind of visibility means that there's more and more people who are transparency reporting and sort of insisting and advocating for certain positions. There's also, I think, a realization by governments that enforcement and the fine associated fines and penalties can actually fund more enforcement.

As that does that, then, you're not taking the, you know, tax dollars to pay for it or government dollars to pay for it. You're taking the money from the wrongdoer and funding enforcement.

And then there the last thing I'd say is that there's this creation of expectations about what an ethics compliance program should look like and what governments will consider reasonable efforts in mitigating your risk. It's just something that you know the US government and other governments globally are publishing. This is what we expect you to do. And if you don't do it, you will have fallen short of what is expected of you. So those are some things I think that have evolved over the last decade or so.

Right. Like everything in ethics compliance, what you're trying to do is figure out what are the use cases, what are people trying to do, what are they trying to accomplish, but then also what risk does that create and how do we mitigate it? How do we get the job done, but do it in a way that doesn't put us at risk or put, you know, those who are associated with us at risk? And so I think when you talk about generative AI, I think you need to start with the idea that this isn't a thing that you can opt in or opt out of.

AI is here. It's no different than when electricity came in or the steam engine or, you know, the industrial revolution. And so the idea is to understand it and try to put structure around how we use it. I think, you know, one of the biggest thing that's I think interesting to me and even outside of governance and mitigation is that you can't do everything that you could possibly do.

No company could use every bit of AI that they could possibly use because there's just not enough time in the day and not enough money to do it. So you have to think about, Okay, let's look at all of these potential use cases. Let's think creatively. Let's think outside of the box, but in the end we have to prioritize our uses that you know what will get us closer to the mission faster.

There are so many different uses that even machine learning that we're we're using, even if we're not using it in house, we have third parties who are using AI and we need to be aware of what those third parties are doing with AI as we, you know, work with them and partner with them and contract with them. We need to know what the limitations are, what their parameters are, what their risk mitigation is, both from a, you know, a security and privacy standpoint, but also what if it's spitting out bad information?

So I think that's important. I think understanding what we have that we're already using and may not recognize as AI, but then also prioritizing, looking at the opportunities and then putting some structure, sub governance structure. Again, not to take away from creativity or the use, but also to just think about how do we do this in the right way.

Yeah. I think the general answer for E and C professionals is that we're definitely gonna benefit from those routine, mundane tasks, those things that require us to do data entry and very basic data analysis.

Everything from investigations to transactional reviews and auditing. I think, you know, contract management and reviews and performance on those contracts, due diligence, media monitoring, adverse media monitoring, a lot of content creation and, training creation, specifically, you know, multi channel, you know, multimodal kind of looking at specific audiences and being able to change that training to fit someone who's in sales versus someone who's in production versus someone who is in a support function. Using AI to do that kind of work, I think is really important. I think the real obvious one is, you know, digital review and just all kinds of data analysis.

But just always remind, you know, my team and myself that you know there's also a personal productivity and team productivity piece to all of this that AI is going to just like email change the way we did work. AI is going to change, you know, the way we do our work and you know, it's a skill that we have to develop. We have to be curious. We have to work on learning about and it is work.

It is work to learn how AI can make us more productive. And there's a little bit of an adoption phase where we become a little less efficient as we learn a new a new process. So if I'm trying to use AI in my in my email system, I'm gonna be a little bit slower. I may have to revert back a little bit to my old email and get get my work done and then say, okay, now I'm gonna try it today and see how this works.

Those are things that are down the road, but I also think that there's a lot of things that we can do with AI. When we talk about gamification and looking at efficacy of training, I think there's a lot of ways there's gonna be room for improvement down the road.

Yeah. My honor. I really appreciate it, Daniel. Jared, thank you so much for having me. I appreciate it.