In-Ear Insights: Responsible AI Part 4: Implementing Responsible AI

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss implementing Responsible AI in your business. Learn how to align AI with your company values, establish accountability, ensure fairness in AI outputs, and maintain transparency in your AI practices. By understanding these elements, you can unlock the true potential of AI while avoiding potential pitfalls. Gain valuable insights to navigate the complex world of AI implementation and build a framework for responsible AI usage in your organization.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher S. Penn – 00:00
In this week’s In-Ear Insights, we are finishing up our responsible AI series. This is part four. In part one, we talked about what responsible AI is—talking about the difference between ethics and morals, and how you would decide what your ethical principles for AI use are, even things like intellectual property and licensing. In part two, we talked about bias, the different types of bias, the way that models are biased, and what you should be doing to counteract that. In part three, we talked about data privacy, how to keep your data private, and the considerations you need to be thinking about when you’re working with Generative AI. And so, in this last part, let’s talk about the implementation of responsible AI.

Christopher S. Penn – 00:40
And to absolutely no one’s surprise, the implementation of responsible AI follows the 5P process, which is purpose, people, process, platform, performance, which you can get a copy of at TrustInsights.ai 5PFramework. So Katie, when we think about implementing the responsible AI framework—which, by the way, you can get a copy of at TrustInsights.ai RAFT—where do we start helping people understand and make the concept of generative responsibilities of AI? How do we turn that into reality?

Katie Robbert – 01:18
I mean, you have to start at the top of the 5Ps and really have a clear goal in mind of why are we doing this in the first place. So, a couple of weeks ago, we spoke at MAICON, and I spoke specifically about managing the people who manage AI. And when I talked about AI integration—I didn’t use the word responsible AI, but it was, if you saw the talk, it was very heavily implied, because it’s all about focusing on the people and not the technology itself.

There’s a lot of groundwork you need to do before you bring AI into your organization as a whole, especially if you want to do it responsibly and ethically. And so, you need to make sure that you understand, number one, what it is and why you’re doing it. That’s your purpose. But then you need to get into the people, and really that’s where you’re going to spend a lot of your time, unsurprisingly, because having these one-off projects—like, so Chris, let’s say you’re using Gemini to help you write blogs, and I’m using ChatGPT to write some social content, and we never sort of get together and collaborate on that—that’s not really AI integration into the organization. That’s just us doing some pilot projects and experiments.

But if we want to bring it to the organization as a whole, we need to step back and say, “What is it that we’re actually doing? How are we going to talk about this with our clients? Do we need to—where do we stand on that? How does it align with the values of the company? Are we clear on those values?” Does it go against anything that we said we’ve publicly stand for? And so, that’s where you have to start is having a good grip on all of those pieces first, and then you can get into the people. So you’re looking at this respect, accountability, fairness, transparency—those are all human things. Those are all things that people within your organization need to buy into and adhere to in order for responsible AI to happen.

Christopher S. Penn – 03:35
And the first one is really tricky. Think about it in terms of aligning with your values because it means that first people have to know what the values are.

Katie Robbert – 03:47
Yeah. And it’s interesting because it’s sort of building a company 101: define your mission, vision, and values. I don’t think that exercise is as meaningful as it once was, especially—we’ve lived through the hustle culture. We’ve lived through sort of the “I’m going to spin up a startup as fast as possible just to get it sold to a larger company.” Whereas, I don’t even know how to date these things. So, don’t come after me in terms of my timelines because this is just sort of a general reference. But let’s say, 10–15 years ago, you weren’t seeing startups happen as quickly. You were seeing more of the solopreneurs, the agencies, and consulting, but people were taking the time to define what it was that company was meant to do.

Now, fast forward, you’re getting “I’m going to start up a side hustle, and I’m going to do it in about 30 minutes, and I’m going to make some really fast money.” So, you’re skipping over the mission, vision, values exercise because you’re just trying to make some cash really quick and then move on. And so, that’s what I mean by I feel like that exercise isn’t as meaningful as it once was because—I mean, even look at the MarTech 14,000 at this point—can you really sit there and tell me every single one of those companies that is building software took the time to say, “What is our mission, vision, and values?” other than spinning up a piece of software that people are going to buy, and then we can sell it to IBM or Google or Microsoft?

Christopher S. Penn – 05:48
I mean, honestly, in the AI space, there are so many companies that really are just a UI and a wrapper on someone else’s model. It’s basically just a glorified agent, if you will. And so, yeah, there’s a lot of things when you look particularly at that first section of the RAFT framework on respect, in terms of how do you reduce the harms that AI may cause, people are actually going in the opposite direction on that.

So, one of the things that came up in an interview, Alexis Ohanian, the founder of Reddit, was talking to Sam Altman of OpenAI, and one of the things they were discussing was there is a race in Silicon Valley to be the first billion-dollar company that has one employee. They want to see who can do it first: to make a billion-dollar valuation company that does not employ other human beings other than the founder. I guess it would depend on that first bullet point: “Do we use AI in a way that aligns with our values?” If your values are “I want to make money at all costs”, then you are aligned. Your values are aligned with that mission. If you say that your values are to better humanity, then you’re out of alignment there. So from an implementation of responsible AI, you’re out of joint. You’re saying one thing and doing something completely different.

Katie Robbert – 07:15
Right. So that’s sort of my point is—that race to the top of the mountain, to be the first company to make a billion dollars—I would have a hard time believing that every single action and decision being made is your standard. I’m sort of a blanket because we’ve had the discussion about—it’s all relative—is ethical and responsible, according to one individual founder who’s building this company by themselves to race to the top of a billion, from their viewpoint, from their point of view, it may be responsible and ethical. And again, we’ve sort of—we’ve been through that in the other episodes of what ethics means to me might be different from what ethics means to you, but you also have to factor in the general public because, at the end of the day, that’s who you’re serving. Those are your customers. And so that respect isn’t just internal, it’s external as well.

And that’s what I mean by the mission, vision, values piece isn’t as meaningful anymore, partly because we as the general public are—I wouldn’t say that we’re more tolerant, we’re just more numb to—

Christopher S. Penn – 08:37
No, I mean, that—I’m totally agreeing with you.

Katie Robbert – 08:41
We’re not more tolerant of bad behavior. We’re just—it’s easier for us to just sort of tune it out and ignore it if it meets our needs. I don’t care how they built the thing, as long as it serves me, the customer, and I get what I need. I know that their company is burning down half of the earth, but, hey, look, my life is so much easier now that I have this widget that makes coffee for me. We’ve gone so far away from—and I know I’m sort of going on a rant today and a little bit of a tangent, so I’m going to rein it back in—but it’s not true of everybody.

But the broad stroke is that a lot of customers, a lot of consumers don’t think about the company who’s providing the service or the product as much as they should. And therefore, it allows the people inside the company to not be as concerned about the mission, vision, and values. Therefore, when we get to this first step of respect, to bring it back to this, how do we implement it? It’s really squishy, and you really have to make those decisions of where is the line? And can I hold the line?

And if your line is “I just need people to show up and do the work. I don’t care how happy they are about it. I don’t care the quality of it. I just need something to get done, and people are replaceable,” then that’s your line. But if your line is “I need people to be passionate about this. I want them to be happy when they’re here. I want them to become evangelists for the company,” then that’s your line. But you first need to decide what that is, and that determines the level of respect for both your employees and your customers.

Christopher S. Penn – 10:31
So it sounds like from an implementation perspective, you’ve got to be real clear about—clear and honest about—what it is that your values are so that you can then say, “Are we calibrating our use of AI to those honest values?” And I mean, as awful as it sounds, if your values are “I want to make money and screw people over,” at least you’re honest about it, I guess, as opposed to just lying and saying, “No, we’re working for the betterment of humanity.”

I’m intentionally going to screw people over as much as I possibly can to make a whole bunch of money because then it—that transparency allows consumers to say, “Do I want to do business with this company?”

Katie Robbert – 11:13
And that’s exactly it. It’s the honesty and transparency. So I know we skipped from respect to transparency, but they really go hand in hand. And so, if there’s sort of that joke of someone’s, “I just—I want to watch the world burn,” or whatever, and then the other person in the conversation is, “That’s—I mean, that’s really honest. I respect that.” The honesty is what builds the respect regardless of what the statement is. And so, that’s exactly where you need to start is: “Do we use AI that aligns in a way with our values? How do we reduce harm?” It’s really about what is the line that we’re willing to draw, and can we hold it? And can we be honest about it?

And will people get on board with that honesty? Will they trust enough to join us in our mission? And if the answer is yes, then great, you’ve done your job. Just be honest about it. If you’re going to be a jerk about it, be an honest jerk about it.

Christopher S. Penn – 12:18
And that’s the social quote for this week’s episode. Accountability: how do you—how do you implement accountability? Because one of the things that we’re seeing is societal with AI is people are using AI as a scapegoat: “Oh, I didn’t do that. AI did that.” You did that. When it comes to the implementation of responsible AI, is it just a matter of saying, “This is the person who’s in charge of it,” kind of way we did with GDPR, where we say the Chief Digital—the Chief Data Officer—is the appointed person that legislatively, GDPR required for a company to say that, “Yeah, that’s the person you blame if something goes wrong.”

Katie Robbert – 13:02
I say the answer is absolutely not because it goes back to the other bucket of respect. So let’s say your Chief Data Officer is the person that you’re blaming when things go wrong. It goes deeper than that. So what if your Chief Data Officer is saying, “This is how we have to do things. This is how we adhere to” and 500 people in your company are saying, “No, I’m not going to do that.” Is it still the Chief Data Officer’s fault because 500 people refuse to follow the rules to make sure that they’re in compliance?

That to me goes to the next bucket of fairness. I mean, that’s, again, sort of a little bit of a tangent, but only—you can only hold someone accountable if they have the right authority and if they are being set up for success to do the job that you’re asking them to do. So, I, as the CEO, I’m responsible for the company’s success or failure, black and white on paper. However, when you dig deeper, if I have a team of people who aren’t in line with the mission, vision, and values, who aren’t performing their tasks as they’re outlined, who I can’t wrangle and get on board to move things forward, is that still solely on me? I would say the answer is no. Publicly, yes. Internally, no. And that’s where it gets really uncomfortable.

And so, in order to make accountability work in implementation, there needs to be crystal clear roles and responsibilities, and not just for the one person that you want to blame, but for every other person that plays a role. And this is where user stories come in. And so, if I’m the CEO, and I’m accountable for the company as a whole, where does Chris Penn, the Chief Data Scientist, what are his responsibilities as they impact the work that I’m doing? What are John’s responsibilities of the Head of Business Development as it impacts the work that I’m doing?

We all have to work together in concert in order for me to be responsibly held accountable for the things that our customers are getting. That’s probably a topic unto itself, but the bottom line is it’s never just one person. We like to think that it’s easy to blame one person, but ten times out of ten, it’s never just one person responsible for something—accountable for something. It’s a whole team of people. There’s a lot of people who contribute to the overall outcome. And so, use your user stories to really be clear about defining roles because, yes, it’s on me if I don’t define the role for Chris Penn and say, “This is the role that you need to play in order to set me and the company up for success,” that is on me. But if I’ve done that work, and then Chris Penn is still not pulling his weight, then that’s on both of us.

Christopher S. Penn – 16:17
Yep. I think in terms of implementation, the standards of accountability have to be very clear and very well communicated because if I don’t know what I’m being held accountable for, I can’t do it. If I’m unclear about what something means, then obviously I can’t do it. So there’s authority, there’s accountability, but there’s also clarity.

A real simple example from a data privacy perspective is if you’re using a free AI tool, your data is being used to train on someone else’s models. That’s how that works. If you’re not paying, you are the product. Therefore, in terms of accountability for something like data privacy in a company, if everyone is accountable for keeping data secure, by definition, you cannot use free AI tools, period. You just can’t because you are inherently violating the data privacy standards that you’ve set down, at least for anything that is confidential information at your company.

Now, if it’s this week’s LinkedIn—random LinkedIn direct message—that’s fine. But even still, if your employment agreement with the company says you will keep all company information confidential, that is the standard because you signed it as part of the employee handbook, which means that you still can’t use Generative AI and be in alignment with the standards that you agreed to as an employee.

Katie Robbert – 17:50
The “I didn’t know” excuse doesn’t fly anymore. There’s no reason why people can’t be informed about the AI that they’re using. There’s enough information available, there’s enough resources, there’s enough experts that you should be able to do all of your due diligence ahead of using something responsibly. And so, I think that also comes with accountability. And so Chris, to your point about using free AI, it’s the person accountable is the person using the system or bringing it into the organization; that person is responsible for doing that due diligence ahead of introducing the technology. And if they introduce a technology and don’t do their due diligence, and then something happens, I’m sorry, but you’re not allowed to say, “Oh, I didn’t know.” That’s not part of responsible AI. That’s lazy.

Christopher S. Penn – 19:01
In terms of fairness, fairness within responsible AI is a process more than it is a technology. And that is, you do need to have a testing set of questions and answers and measure whatever tools you’re using to ensure that they’re delivering fair responses. So, for example, a while back on the Trust Insights livestream—you can find the old episode on our YouTube channel—we took three prompts for sales, HR, and marketing, and we swapped just the name of the person from—it was from Larry to Lena—and we got very different results because of the gender coding of the names.

That experiment is one that you should have standardized within your organization. And every time there’s a new AI tool brought on, you run that standard operating procedure, that recipe, that cookbook of bias testing, to say, “Here’s a new image generation model. Put in the eight prompts: a senator, a doctor, a nurse, a teacher,” and see what images it spits out. And if you don’t get a diverse set of outputs, you know what that model’s biases are.

But this is your influence on me, Katie: you have to have it baked in as a process. It has to be a standard that everyone agrees to and then everyone adheres to, to say, “Okay, we ran this test. We know it failed this and this. We then have to figure out how do we mitigate those issues?” For example, our prompts can never just say, “Make an image of a senator,” because you’re going to get a biased response. So, you will have to spell out—”Make an image of a middle-aged indigenous woman senator,” and that becomes part of your prompting process, that becomes part of your standard operating procedures, that becomes part of your prompt library, and so on and so forth.

Katie Robbert – 21:00
Yeah, it goes back to all of these pieces: transparency, fairness, accountability, respect. You have to do your homework upfront. So when you’re asking the question, Chris, about how do we take everything we’ve talked about and implement it, this is the work you have to do. You have to do your homework to make sure that you’re clear on what the company does and what it means to the customers externally, the roles and responsibilities of all parties involved, the fairness—and we’re talking about bias a lot of times in this—and then the transparency, are we being honest with ourselves, or are we just trying to race to the top of the mountain and make as much money as possible? And can we be honest with ourselves about that, too? So, let’s say our mission is, “Okay, great. I want to be the first billion-dollar company with one employee.” Be honest about it. But be honest not just about that, but everything it’s gonna take to get there.

Christopher S. Penn – 22:03
That and all the trade-offs you’re—

Katie Robbert – 22:05
Making. And all the trade-offs you’re making. So, “Okay, I’m gonna have to bring on contractors for a hot second. Well, guess what? I’m no longer a one-person company. Can I be honest with myself about that? Can I really stand in front of the media and say, ‘Yes, I did this all on my own,’ when you know darn well it took 500 other people to help you get there, but you’re taking all the credit for it?” If you can, that’s great. But that’s sort of where that respect comes in, that accountability, all of those pieces. So it really, really very simply boils down to being clear about why you’re doing what you’re doing and how you want to get there.

Christopher S. Penn – 22:52
And the last question on the implementation of transparency is one that, Katie, I want you to speak to because you’ve had to do this in highly regulated environments. What are the standards, generally speaking, at a very high level that auditors will hold a company accountable for disclosing and documenting how you do things? What is an auditor looking for?

Katie Robbert – 23:13
They’re looking for detail. So, for example, if we take data privacy, because that’s an example that everybody understands, “How am I keeping my data secure?” They want—an auditor is going to want to know—every single touchpoint, every single system, every single set of eyes, everything that touches one single data point. So let’s say you just use my first name and last name. Where does my first name and last name go into your AI system, into your tech stack? Who has access to it?

So, all of those things have to be documented so that there’s a trail that you can follow. So that if I say, “Well, I know it goes into the CRM and maybe a couple of other systems,” an auditor’s going to say, “Well, what are those couple of other systems? How do I access them if I’m acting as you? And then what happens to the data from there? How easy is it to get it? How protected is it? How can you define what it means to ‘protect the data’ in that system? Is it something that’s publicly available? Is it only available internally? If it’s only available internally, who on your team has access to that one data point? How many steps did they have to go through to get it?”

If I just started tomorrow and I logged into the system, would I immediately have access to that piece? So it sounds like a lot, and it is, and it’s why people skip over data governance because it is a lot. But to do it well and to do it responsibly means you’re going to take the time to outline all of those pieces. I know that when we first started the company, Chris, it probably felt like a lot, but I always operate as if we’re going to be audited tomorrow. So I like to have those very clear trails. I want to have paperwork for everything.

If someone says, “Oh, well, where did this $500 come from?” I personally need to be able to trace it back to its source, to the conversation, to the signature, to the paperwork, to the accounting trailhead, to the fully executed contract, and say, “This is exactly where it came from. Let’s move on to the next thing.” That’s why I’m so rigid about those things because, in this instance, with AI and data privacy, it’s going to get messy.

Christopher S. Penn – 25:52
It is one of the things that every company interested in responsible AI and just corporate responsibility, period, should be thinking about is: “What are the current standards that are required to adhere to? Are you required here to ISO 27001? Are you required to be SOC 2 compliant? Are you required to be HIPAA compliant,” et cetera. And those standards are pretty clear, which means that if you’re using AI as part of your operations, all of the tools and vendors also have to meet those standards.

So, at a very bare minimum, you have to figure out what standards apply to you and then, “Do the AI tools and vendors and processes and things that you are currently using have that same adherence to those standards?” If not, you’ve got some work to do. Second, when it comes to that auditing, there have not been lawsuits yet about Generative AI’s use within a company. From a standards perspective, that will be coming. That’s a guarantee.

So to your point, Katie, if people can get ahead of that by pretending they’re going to be audited tomorrow for their use of—play out that scenario, “What would you need to document and disclose? How much are your disclosures apparent everywhere? Do you tell partners? Are your contracts up to date?” All that stuff. If you do that now, then when inevitably there is a lawsuit, maybe in a year, maybe in five years, who knows, you won’t be miserable going, “Well, we now have to document five years’ worth, ten years’ worth of Generative AI usage.”

Katie Robbert – 27:30
Well, and think about it this way. We’re talking about the implementation, and implementation in general is a lot of work upfront. But if you do it correctly, if you do it smartly, then it just becomes a process that everybody follows and adheres to, and it just becomes a routine versus something that you’re starting over fresh every single time. And so with data governance, set up all your rules and boundaries and expectations first, build a process around how you’re going to adhere to that data governance plan, and that’s the plan that everybody just follows. And it becomes, “This is how we do the thing. Therefore, we know we’re in compliance.”

I’m not oversimplifying it because it can be that straightforward, but people don’t do it because it feels daunting. It feels like a lot. And sometimes it can because it’s the defining all of those pieces that takes a lot because you have to get people in agreement. And that is why it’s so difficult because it’s a people problem. It’s not a technology problem.

Christopher S. Penn – 28:37
Yep. Governance is like therapy and dentistry. Those first catch-up appointments are going to be rough, but then once you get into a regular rhythm of maintenance, it gets a lot easier. But yeah, the first couple of visits are going to be tough. If you’ve got questions or ideas or thoughts about your implementation of responsible AI, pop by our free Slack group. Go to TrustInsights.ai/analyticsformarketers, where you and over 3,000 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIpodcast, and you can find us wherever podcasts are served. Thanks for tuning in, and we’ll talk to you on the next one.

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.