In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the meaning of “responsible AI,” a term that feels like a buzzword, and how it lacks a common understanding. You will learn why aligning AI with your company values is crucial for reducing harm and ensuring ethical practices. Katie and Chris introduce a five P framework for implementing responsible AI and explore the importance of transparency and accountability. Tune in to discover how to navigate the evolving landscape of AI and its implications for your business.
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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, this is the first of a four-part series we are doing on a topic that, according to our ICP, we should have done a long time ago, which is looking at responsible AI. What the heck is this stuff? What are the major issues in it? And things like that.
So, responsible AI generally falls—has, like, four components to it: fairness, transparency, accountability, and human centeredness. But one of the things I have a problem with, Katie, and I want your take on this, is responsible AI seems like one of those blanket terms that says nothing in the same way, like, responsibly sourced food. Like, what does that mean? So, to you, as someone who’s maybe not as much of a technical nerd as me, what does responsible AI mean to you?
Katie Robbert – 00:55
Well, it falls into the bucket of, like, corporate responsibility and all of those other things that just kind of feel like buzzwords and just sort of, like, let me just sort of talk the talk.
Katie Robbert – 01:10
But I’m not going to walk the walk.
Katie Robbert – 01:11
So, if someone says, “Our company practices responsible AI,” I’m definitely going to raise an eyebrow at them, because that doesn’t mean anything. What it should mean is sort of—I mean, you have to take a step back and figure out, first and foremost, what does responsibility mean to you and your company. So, it’s going to mean different things. And I think, Chris, we’ve had this conversation before in the context of ethics and morals, is that it’s going to be—
Katie Robbert – 01:47
There is no one right answer.
Katie Robbert – 01:49
There’s a general answer, but then when you get into the details, like, what I think is moral and what you think is moral, they’re going to be similar, but there’s definitely going to be—like, when you get into the minutiae, there’s going to be differences just because of our upbringing
Katie Robbert – 02:08
And our way of thinking and our belief system and all those things.
Katie Robbert – 02:11
And so, when you talk about corporation as a whole, your first challenge is getting all of the people in that corporation to share a thought. And that’s—that’s number one, that’s really hard to do. And number two, when you tack on the term responsibility, then it comes with a lot of weight. And so you have, accountability. You have things that—
Katie Robbert – 02:43
Publicly, you want people to know that you’re doing, but you actually have to—
Katie Robbert – 02:46
Do them and have proof to demonstrate that—
Katie Robbert – 02:48
You’re doing those things.
Katie Robbert – 02:49
And so, to go back to AI—
Katie Robbert – 02:54
Responsibility, you first and foremost have to define what that means.
Katie Robbert – 02:58
And so, ideally, it would mean, things like, “we’re not using it to do any harm, we’re being transparent about it.
Katie Robbert – 03:07
This is what our data governance looks like.
Katie Robbert – 03:09
These are the frameworks that we’re using.”
Katie Robbert – 03:12
And so, outlining the way in which artificial intelligence is being used in a company.
Katie Robbert – 03:19
And the responsibility part is that you’re not just saying it, you’re actually doing it and you’re being transparent about it.
Katie Robbert – 03:27
Whether it’s on your website or with—
Katie Robbert – 03:29
Your clients or whatever that looks like.
Katie Robbert – 03:32
Whatever makes sense.
Katie Robbert – 03:33
If you’re a publicly held company, it—
Katie Robbert – 03:36
Better be on your website and it better be something that people can easily—
Katie Robbert – 03:39
Find. If you’re a company like Trust Insights—again, this, it’s case by case. Our responsibility is to our clients, so we don’t necessarily have to put it on our website. It’s a good idea, but the only people we’re responsible to is our clients
Katie Robbert – 03:55
Because they’re the ones who are paying customers. We’re not publicly held.
Christopher S. Penn – 04:00
So, I think that’s a really great way of looking at it. Are you reducing potential or actual harm? That’s basically what responsibility comes down to. You will hear that in political discourse in the US about things like irresponsible gun ownership, for example. It means you’re reducing harm, you’re reducing potential or actual harm. So, when we think about AI, then governance is part of it. What about—what about the willingness of a company to say, “Huh, that system’s misbehaving. Maybe we should turn it off,” with the acknowledgement that for some companies that might be like their bread and butter, that might be the secret sauce. How should an executive think about responsible use of AI when your company literally depends on it?
Katie Robbert – 04:51
And this is where it starts to get really difficult. And so, in a—
Katie Robbert – 04:57
Perfect world, and again, I recognize that—
Katie Robbert – 04:59
A lot of times when I talk about these things, it’s sort of an idealistic viewpoint. But in a perfect world, as you’re thinking about introducing artificial intelligence into your—
Katie Robbert – 05:09
Organization, especially if your services and products—
Katie Robbert – 05:13
Are heavily reliant on it, you’ve had—
Katie Robbert – 05:15
The conversations and you’ve outlined those scenarios.
Katie Robbert – 05:19
The, the disaster scenarios of—
Katie Robbert – 05:24
“What do we do if AI goes sideways and it misaligns itself with our mission?
Katie Robbert – 05:30
Are we willing to pull it back?”
Katie Robbert – 05:33
I can almost guarantee those conversations have—
Katie Robbert – 05:35
Not been had at companies. Because there’s part of it is a—
Katie Robbert – 05:41
Lack of education that can happen.
Katie Robbert – 05:43
And some of it is just willful ignorance that, “well, we’re here to make—
Katie Robbert – 05:47
Money, it’s just a business.
Katie Robbert – 05:48
And, like, we’ll deal with all that crap later.”
Katie Robbert – 05:50
And the dealing with it later is what gets a lot of companies into trouble.
Katie Robbert – 05:54
So, in an ideal world, the way to think about true AI responsibility is—
Katie Robbert – 06:01
To plan those things out before they even happen.
Katie Robbert – 06:04
And so to say, “All right, let’s say we build Katie GPT and she’s going to take over 90% of Katie in real life’s job.
Katie Robbert – 06:17
We’re making that investment. Are we comfortable making that investment? What happens if she goes sideways? We’re suddenly going to lose a lot—
Katie Robbert – 06:29
Of money, potentially lose a lot of—
Katie Robbert – 06:31
Audience because KDGPT has gone off the rails. Are we willing to take that risk?”
Katie Robbert – 06:37
Because it all comes down to how—
Katie Robbert – 06:39
Much risk you’re willing to take. At the end of the day, it’s—
Katie Robbert – 06:42
Software, and software comes with risks.
Katie Robbert – 06:46
You have to acknowledge how much risk—
Katie Robbert – 06:49
You’re willing to live with.
Christopher S. Penn – 06:52
I like—I really like that framework of, if you were to take your existing values, things that your company says you are about, and apply that to AI, to say, “What would happen? How could AI violate these values?” So, if you, for example, said, “We are cheerful and agreeable,” and we turn K to GPT into a morose sort of goth personality, very nihilistic, that would be a case where the software is running afoul of the values. Now, obviously, if your company’s values are harm inducing to begin with, then your AI is probably going to follow suit. If you’re—and if your actual practiced business practices do not reflect your values, you’re going to have a hard time aligning AI with your values. If your business does something opposite.
Christopher S. Penn – 07:50
If you say, “Our employees are our most valuable asset,” but actually, no, your shareholders, are your most valuable asset, and your employees are disposable cogs, then when you implement AI, it’s going to prioritize the data that you give it. And, the data you give it is skewed towards making your shareholders richer and not treating your employees well.
Katie Robbert – 08:09
And you said something that I want to go back to, which is if your values—you know, are to reduce harm, that’s going to be some—that’s going to mean something different to different people. And so, I mean, we can see this playing out every day in our—in politics, in—you know, the economy, like—pick a thing! And, people’s beliefs of what is harmful are vastly different. It’s—it’s—I don’t—
Katie Robbert – 08:42
Want to get into the weeds with—
Katie Robbert – 08:44
It, but the point being is that—let’s say I’m a company—that this is totally fictional—that believes in—you know, mass producing automated rifles, because I believe that it’s going to—
Katie Robbert – 09:05
Make the world a better place.
Katie Robbert – 09:07
It’s going to give people—
Katie Robbert – 09:11
Some kind of safety.
Katie Robbert – 09:13
Like, I’ve created it in such a way that it’s a good thing. I don’t believe that I am doing harm. Therefore, my AI responsibility is to use—
Katie Robbert – 09:28
It in such a way that aligns with my belief system, with my mission.
Katie Robbert – 09:33
My mission being to give more people access to automated weapons. Again, totally fictional.
Katie Robbert – 09:41
I do not believe that is—
Katie Robbert – 09:42
The case, but there are going to be companies out there where that is—
Katie Robbert – 09:48
Their belief system, that is their mission.
Katie Robbert – 09:50
And that’s why when you talk about responsible AI, corporate responsibility, ethics, morals, it’s such a heated topic of conversation because everyone’s entitled to their own way of thinking. You don’t have to agree with it. So then, your corporate responsibility, if you—
Katie Robbert – 10:16
Find that there’s—you know, a competitor—
Katie Robbert – 10:18
Out there or another company that could—you know, put your audience in danger, whatever the situation, your corporate responsibility is—
Katie Robbert – 10:28
To take steps to protect against that. And, that needs to become part of your roadmap.
Katie Robbert – 10:32
Part of your plan for your responsible AI is, “We will protect against things that are harmful,” and that includes these—
Katie Robbert – 10:42
Outside companies, these outside beliefs. And again, it gets really messy.
Christopher S. Penn – 10:47
It gets really messy. But, I think there’s at least some common ground that we can come to. And, the common ground we can come to, unsurprisingly, comes from here. Here’s how I think—and I want your take on this—here’s how I think that we implement responsible AI with the five P’s. First, we have to be clear on our purpose. What is the AI supposed to do? And, is that aligned with our values?
Katie Robbert – 11:11
Right.
Christopher S. Penn – 11:12
If you’re—if you’re building image recognition technology and it is discriminating against people of a certain ethnicity, is it supposed to do that? If the answer is no, then obviously you have an issue. The second P is people. Who is accountable? What person is accountable for the system? Because no AI is self—is sentient or self-aware. Therefore, a human being at the end of the day has to be the one saying, “Yep, that—I did that. That’s my fault.” Third is on process. Are the systems behaving in the way that is expected? Are they being fair? Are they expressing biases through things like your prompt structure? The fourth is on platform. Can you explain what the system is doing? If you can’t take apart the platform into its component pieces and say, “This is what this piece does,”—
Christopher S. Penn – 11:58
Then you don’t have a transparent or explainable system. And then, the fifth is you measure the outcome of the machine to say, “Does this align with our purpose?” If you’re not measuring the output of AI, then obviously you’re not accountable. You’re not behaving in a responsible way. It’s like, “Hey, I made a new car. We don’t measure any safety features on it.” Well, then it’s not a safe car if you’ve done that. So, what do you think about using the five P’s as a framework for responsible AI?
Katie Robbert – 12:28
I think it’s a really great framework in terms of capturing your requirements, in terms of transparency, and in terms of accountability. And those are sort of, the key tenants of responsibility is you have to say what you’re doing, you have to be transparent about it, and then you have to actually do the thing. And so, that brings us back to—
Katie Robbert – 12:55
Where we started in the conversation. And so, the five P framework is a really good place to start for that because you do—you clearly define your purpose. You say, “This is why we’re doing it.
Katie Robbert – 13:06
This is—you know, what we’re doing it for.”
Katie Robbert – 13:09
To your point, you have the person—
Katie Robbert – 13:11
Who’s accountable. But, to that you can build on who’s in our audience, who—
Katie Robbert – 13:17
Is going to be the recipient of the thing that we’re doing, who is going to be maintaining the thing that—
Katie Robbert – 13:23
We’re doing, who is going to be watching—
Katie Robbert – 13:25
There’s a lot of different roles, both internally and externally. Then of course, you have your process of “here’s how we’re doing the thing”.
Katie Robbert – 13:33
That’s probably, in this particular context, one—
Katie Robbert – 13:37
Of the most important P’s is “How we’re doing the thing.” And, this is where your data governance comes in.
Katie Robbert – 13:44
This is where your—you know, sort of your guardrails live in terms of, “this is what we are and aren’t doing.” And then, your platform, which is the tools and software, and then your performance.
Katie Robbert – 13:59
Did we do the thing? And so, I think that for companies that are looking to start off with—
Katie Robbert – 14:04
With—our clients have been asking, “How are we using AI?” or “We want to put some sort—
Katie Robbert – 14:12
Of statement out of ‘here’s how we’re using AI in a responsible way’.”
Katie Robbert – 14:16
I think the five P framework is a really good place to start to frame out that messaging because it hits—
Katie Robbert – 14:23
On all the really important points.
Christopher S. Penn – 14:28
Should they have a—should companies have a template, based on the five P’s, where they can just answer a bunch of questions and then essentially have their responsible AI statement?
Katie Robbert – 14:40
I think they should because—
Katie Robbert – 14:43
You may not start off getting a—
Katie Robbert – 14:44
Lot of those questions, but as the technology evolves and as the usage of—
Katie Robbert – 14:50
It increases, you will get those questions.
Katie Robbert – 14:52
I know that a lot of our clients have already gotten questions around, “Well, if you’re using AI then—
Katie Robbert – 14:58
I don’t have to pay you as—
Katie Robbert – 14:59
Much because it’s just expediting things.” And it’s a misunderstanding of how—it’s a disconnect between that client and their clients of how they’re using artificial intelligence. And so, I think that having—you know, going through the exercise of the—
Katie Robbert – 15:16
Five P’s gives you that clarity to—
Katie Robbert – 15:19
Say, “Well, this is the response we—
Katie Robbert – 15:20
Can give.” Because what you don’t want—
Katie Robbert – 15:22
To do—again, idealistic—is—
Katie Robbert – 15:25
Get defensive about it.
Katie Robbert – 15:26
Like—
Katie Robbert – 15:28
“No, we have to—
Katie Robbert – 15:28
Keep charging you the same. If anything, we’re going to charge you more because we’re using advanced technology.”
Katie Robbert – 15:32
And it’s like, that’s not a useful conversation. You want to be just very clear—
Katie Robbert – 15:37
And direct, like, “This is what we’re doing, this is why we’re doing it. This is how we’re doing it.
Katie Robbert – 15:42
This is how it benefits you. Therefore, this is the value of it.”
Christopher S. Penn – 15:48
I think maybe we should make a custom GPT or something that can walk people through building a responsible AI statement. I think that would be an interesting exercise to do sometime.
Katie Robbert – 16:00
I would actually say what you want—
Katie Robbert – 16:02
To do is build—
Katie Robbert – 16:05
A custom model—
Katie Robbert – 16:06
That walks you through the five P’s.
Katie Robbert – 16:08
And then one of the use cases is an AI-responsible statement.
Christopher S. Penn – 16:14
And we’d have to have—probably want to have at least a lawyer check it over to make sure that you’re not saying something that’s highly risky.
Katie Robbert – 16:23
Well, and sort of that’s sort—
Katie Robbert – 16:25
Of the other half of the conversation.
Katie Robbert – 16:27
Is—AI is great. AI does a lot of things, but—
Katie Robbert – 16:31
It still needs it—
Katie Robbert – 16:32
You know, it should have a lot of human intervention.
Katie Robbert – 16:36
I was having this conversation with one of our clients last week because they were sort of—they’re newer to AI.
Katie Robbert – 16:41
And they were asking about—you know—
Katie Robbert – 16:43
“What does the process look like?
Katie Robbert – 16:45
You know, is it start to finish?” And I’m like, “No, people still need to be involved.” And that—you know, if you’re building out an AI responsibility statement, you could—
Katie Robbert – 16:54
Include, “And all of this has been checked by our human lawyer.” That can—
Katie Robbert – 16:58
Be part of your people—you know, statement, part of your data gathering to say, “We have lawyers, they have looked—
Katie Robbert – 17:06
This over and they are okay with us saying these things.
Katie Robbert – 17:08
We’re not causing harm.” Back to—you know, the values. “We’re not causing harm to our company by—
Katie Robbert – 17:14
Putting this information out there.”
Katie Robbert – 17:16
It’s a big part of it.
Christopher S. Penn – 17:19
I think I’m going to—at the very least—put together a—yet another framework. I’m going to call it “raft” because I think it’s something that is a good starting point. So, respect for human values, accountability, fairness, and transparency as the four linchpins for where you should be thinking, applying those principles to each of the five P’s. When it comes to gender, AI in the people, is there a respect for human values?
Is—are we saying human—that AI should support humanity and not detract from it? Is there an accountable person? Does it treat people fairly? Is there transparency available to the affected parties for how AI does things? I think we could probably make a very interesting framework for helping people get granular with five P’s when it comes to responsibilities of AI.
Katie Robbert – 18:20
The one modification I want to make—so, the couple of times now that—
Katie Robbert – 18:25
You’ve mentioned accountability, you keep trying to—
Katie Robbert – 18:27
Single it down to one individual person. When you talk about something like corporate responsibility, sure, you can sort of put—
Katie Robbert – 18:35
Everything on the shoulders of the CEO, but really it’s the actions of the—
Katie Robbert – 18:41
Company as a whole.
Katie Robbert – 18:42
So, the company as a whole has to be accountable.
Katie Robbert – 18:46
You know, if you have to—like—
Katie Robbert – 18:48
Pick one person, yeah, then you pick the CEO.
Katie Robbert – 18:52
But, I want to sort of clarify that it’s not solely their responsibility to hold the company accountable. The company as a whole has to be accountable. And that comes from being clear about—
Katie Robbert – 19:08
Your purpose, being clear about what you’re doing, so that people will get on—
Katie Robbert – 19:11
Board and actually do the thing. And so, I just want to make that clarification.
Katie Robbert – 19:16
I understand that you’re saying, like, “Who’s—
Katie Robbert – 19:18
The one person who has to answer to it?” But it’s an unfair notion to think that one person can make everybody—wholly accountable.
Christopher S. Penn – 19:29
Yeah, I was thinking about that based on the way GDPR is implemented, where companies who are required to adhere to GDPR must have a data protection officer. It is a requirement of the law that—and I would expect that as AI continues to evolve, there will be someone similar who’s like an AI protection officer. Like, yeah, your job is literally to identify and mitigate and answer, on behalf of the company, those risks. Now—you know—with GDPR, it’s usually the CIO or the CTO that is also the data protection officer, but not necessarily, not always. There’s sometimes the CDO, but in bigger companies they have to have a full-time person whose job is to answer the EU courts.
Katie Robbert – 20:17
So, if you think about that in terms of the five P’s, that becomes—
Katie Robbert – 20:22
Part of your requirements gathering.
Katie Robbert – 20:24
So, what is the purpose? Who are the people—based—
Katie Robbert – 20:28
On what we want to do.
Katie Robbert – 20:30
We have the people in place to perform this function, to—you know—do the thing? And, if the answer is no, then—
Katie Robbert – 20:38
Perhaps you need to pause for a—
Katie Robbert – 20:40
Minute and figure out the skill sets. Because if you have four of the five P’s, that’s not good enough. You have to have all five in order for it to be—something that’s valid. You can’t have people, process, platform, performance, without a purpose.
Katie Robbert – 21:01
You can’t have purpose, process, platform, performance—
Katie Robbert – 21:04
Without people. And so on and so forth. And so, you need—they all need to work together. So, if you don’t have someone in your organization who is comfortable owning AI responsibility, you’re not ready. Period.
Christopher S. Penn – 21:21
It’s like Pokemon. Gotta catch ’em all!
Katie Robbert – 21:25
Sure.
Christopher S. Penn – 21:29
All right. In part two of this, we’re going to talk about responsible AI specific to things like biases and prejudices and how that impacts your data. So, you’re going to want to stay tuned next week for that episode. If you’ve got some thoughts about what responsible AI means to you that you’d like to share, pop on by our free Slack group. Go to trustinsights.ai/analytics4marketers where you and over 3,500 marketers are asking and answering each other’s questions every single week about data and AI. And wherever you watch or listen to the show, if there’s a channel you would rather have it on instead, chances are we have it. Go to TrustInsights.ai/tipodcast. You can find us where podcasts are served. Thanks for tuning in and we’ll talk to you next time!
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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.
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