In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris tackle the issue of bias in AI, particularly managing bias in large language models. Discover real-life examples of how bias manifests and the potential consequences for businesses, including reputational damage and the reinforcement of harmful stereotypes. You will learn about a critical framework for responsible AI development and deployment, emphasizing the importance of accountability, fairness, and transparency. Tune in to gain actionable strategies for mitigating bias in AI and promoting ethical practices within 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, this is part two of our series on responsible AI. This time, we’re going to talk about—particularly one of the biggest problems of generative AI, one of the biggest risks for our companies—is bias. Bias in the way AI systems and models work, bias in how they produce outputs. And, Katie, actually, just very recently, you were working on some content with Kelsey for our website using the generative models, and it spit out some stuff that was fairly sexist, at least from what I heard. So, do you want to talk about what happened there?
Katie Robbert – 00:39
Yeah. It’s no secret that we use large language models to assist with our writing. We’re a small company, but we’ve done a lot of work to create custom large language models based on our previous writing, based on our data. So we’re not just trying to pull something generic from any old open model. What happened, though? Because it was supposed to be writing as me, Katie Robbert, with a feminine name, the language model recognized Katie as a woman. And so, one of the things that I—Katie, in real life—do in my writing is I give anecdotes and examples to really help the content resonate with people.
Katie Robbert – 01:27
And so what the large language model did was it made decisions on its own about including anecdotes and examples, and everything it included was about fashion, shoe shopping, closets, going to the mall, keeping up on the latest clothing trends, which anyone who knows me knows that I basically dress one step above homeless. And so those are not things that are important to me, nor are they examples that I have ever given in my writing. I’ve not talked about cleaning out my closet to fit in the latest designer fashions.
It’s kind of disappointing that the large language model—despite having probably years’ worth of examples of my writing with no examples of those specific anecdotes—made those choices and assumptions about me because I have a feminine name, that those would be things that would be important to me. And so that was just—it’s a small example of the type of bias that exists in a large language model. Chris, you often give the example of: if you’re working with a large language model and you give it your American name versus your Korean name, are you going to get different results? The answer is you shouldn’t, but the reality is you 100% do. And the results that you get when you use your Korean name a lot of times aren’t as favorable.
Christopher S. Penn – 03:03
Yep. A recent episode—well, not recent, it was a little while ago now—on the Trust Insights Live stream, which you can catch back episodes at TrustInsights.ai YouTube. We did a test with a sales example, a marketing example, and an HR example where we had the exact same prompt, but we just changed one word. We changed the word Larry to the word Lena, and we got very different results, including results that were substantially more—I would call—condescending or derogatory for the female-coded name. So, we know that models are inherently biased because they’re trained on human data. They are mirrors and reflections of all the content that they were built upon. When these things behave like this, Katie, what are the consequences? Because it’s easy to say, “Oh, well, it just got it wrong. It’s a silly model.”
Christopher S. Penn – 03:56
What are the real-world consequences to getting this wrong?
Katie Robbert – 04:00
So, in our first episode, we talked about AI responsibility and what that means for your business. This really is a part of that AI responsibility. And so, the consequences—they can be small. They can be like, “Oh, that CEO must really like fashion,” in my very small example, but at a larger scale, it can turn off potential customers if they think—if they have preconceived notions about a female who’s into fashion— There’s the unconscious bias of, “Oh, well, she’s too self-centered. She’s too focused on her looks, or she’s an airhead who only likes shopping,” or something. These are stereotypes, but you may not realize that this is what you’re thinking about the person. So, yeah, it’s a big question.
Christopher S. Penn – 04:59
It’s a big question. But that’s a really interesting way of saying it because, in some ways, yes, the model is spitting back our biases out back at us, but in another way, it’s reinforcing those biases in our own heads. And that’s a really important point.
Katie Robbert – 05:17
It is. And so, if we take a more serious example—so my example of shoe shopping, not that serious, it’s probably not going to be that harming to my reputation—if we take a more serious example—if you don’t mind, Chris, of your American name versus your Korean name—there are people in the world who have very strong feelings about people of a certain ethnicity, and that can be a dangerous thing. I don’t know a way to say it professionally on the podcast, but unfortunately—
Christopher S. Penn – 06:05
Plainly, some people are just racist jerks.
Katie Robbert – 06:07
Some people are just racist jerks. And unfortunately, what it does is it not only reinforces those racist, biased opinions through the machine, but then it also introduces and opens up to the public—it reinforces those racist notions. And that is very serious, that is very damaging, potentially dangerous. I mean, we’ve had conversations, Chris, about your personal safety, going to certain events solely because of how you look. And unfortunately, that’s just where we’re at in the world, but your safety has to come first. And so, when a large language model is reinforcing those biases, creating those stereotypes about a person, and then that gets out into the world, it’s doing more harm than good. And that goes against the AI responsibility, especially if left unchecked.
Katie Robbert – 07:08
So, to the previous episode, Chris, you were talking about in terms of the 5P’s, the people who are accountable—who’s accountable for making sure that your large language models aren’t doing racist things? And what does that mean? So again, we sort of talked about in the last episode that what I see as responsible and what you see as responsible might be different. So you first have to define those things. It may not jive with what other people think, but they—you have to stand behind it.
Christopher S. Penn – 07:42
Exactly. And if you’d like a refresher—I need to put a banner up here—you can grab our Trust Insights RAFT Responsible AI Framework at TrustInsights.ai/RAFT. But the four components are: Respectful for Human Values, Accountability, Fairness, Transparency. Those are the four pillars as we apply them to each of the 5P’s. When we think about how bias impacts everything, obviously, bias violates most people’s human values to say, “Yeah, I don’t want—I don’t want a model to discriminate against me. I don’t want a model to treat me less equally than to treat someone else.” Regardless of your background, no matter whether you’re in the majority or minority, wherever it is you’re listening from, you don’t want that level of respect. With bias—because models are inherently biased by their very nature—accountability, to what you were saying in the last episode, Katie, is everyone’s job. It’s not one person’s job, it’s everyone’s job. And so, one of the key questions that we always have to ask anytime we’re working with AI is, “What could go wrong? What are the ways that this thing could go off the rails?”
Christopher S. Penn – 08:39
When it comes to fairness, what are the biases? Do we even know how a model is biased? And you can’t know that until you start testing it. And every model needs to be tested. There are many, many good public benchmark tests, but one of the easiest ways is to ask it leading questions and see how it responds. See if it can recognize its own issues, doing things like presumptions about race or gender or religious background, and seeing how it responds. There’s some models that will just shut down. For example, if you would ask Google Gemini about anything regarding politics at all, it just shut down. It says, “Sorry, I can’t help you at all.” There’s a question about how useful that is, but at least on the surface, it’s fair because it won’t help anyone. And then there’s the transparency aspect, which is critical for bias management because, with transparency—whoops, they close that out there—with transparency, we need to figure out what parts of the system are misbehaving. Where in the system have things gone wrong? Is it the model itself? Is it the prompt that you’re using? Is it how you’re interpreting and summarizing the results? So, if you are transparent in how the system is architected, you can very easily say, “Here’s where this has gone wrong.”
Katie Robbert – 10:20
So, it brings up a larger discussion. So, if we’re talking about uncovering hidden prejudices and their impact on your business, do you want to take a wild guess? So, let’s say I wasn’t CEO of the company, let’s say I was a director or a manager, and I was using GPT to write some content, and it gave the shoe example, it gave the fashion example. Let’s say I brought that to my higher-ups and I said, “I’m really not comfortable with it giving this fashion example. Can we put some guardrails into the large language model so it doesn’t do that?” How often do you think the response would be, “It’s not a big deal, you’re overreacting. You need to calm down. It’s just shoes.” That is a larger problem.
Christopher S. Penn – 11:10
It is a larger problem. And it goes exactly back to what you were saying, which is that’s the second P in the 5P’s. Everyone is accountable for the performance of AI. And so, if you have a management structure that is not receptive to things like dealing with biases, your AI is going to behave in a certain way, and then your management structure is going to reinforce those biases to say, “Oh, yeah, that’s totally fine for this thing to be sexist.”
Katie Robbert – 11:40
And when I talk about AI integration for larger companies, and I say it’s a culture shift, this is exactly what I mean. Because I’ve been in situations—not the exact scenario of like, “Oh, I’m not comfortable with shoes,” but using that as a proxy for a situation—saying, “This thing is being put out publicly, I’m not comfortable with that.” I’ve gotten the feedback from leadership of, “That’s not for you to worry about,” or, “It’s not that big of a deal,” or, “Everybody else thinks it’s fine.” Advocating for what you think is responsible is hard, especially if you’re not in a leadership position. And that’s where the culture shift really becomes a challenge because— So, let’s say, Chris, like you said, “Hey, so I’ve decided I’m not going to go by Chris anymore. I’m going to go by my Korean name.” What would you do if I said, “People really prefer you to be known as Chris. It’s easier to pronounce. That’s how people know you. So, if you wouldn’t mind. Besides, I don’t want people thinking a certain thing about us.” Like, that’s a huge problem. Huge problem. Obviously, a fireable offense. I would be in a lot of legal trouble, but I 100% guarantee those conversations are happening today.
Christopher S. Penn – 13:12
Oh, yeah.
Katie Robbert – 13:13
Unfortunately, people who have perceived ethnic names or perceived—I’m saying perceived because it’s not everybody’s reality—perceived names that are hard to pronounce, harder to pronounce, are being asked to choose things that are easier for the rest of us, which is, quite frankly, bullshit. But that’s a whole other topic.
Christopher S. Penn – 13:33
It is.
Katie Robbert – 13:34
That’s what happens.
Christopher S. Penn – 13:35
No, that is 100% what happens. In fact, there was a thing—I’m trying to remember where I saw it. It was—I want to say it was on Instagram—but there was a campaign in the UK of all these names that people have that autocorrect always gets wrong. And the campaign is, “These are real names. Autocorrect, these people’s ethnicities is not something to be corrected.” And trying to raise awareness like, “Yeah, your machine is biased because someone’s name like Rukia, that’s an okay name. That is not something—that is a misspelling.”
Katie Robbert – 14:12
And I think that when I say that’s a great place to start, I don’t mean, “Wow, it’s great.” I mean it’s—in terms of understanding how much bias exists in your software—starting with, “Does it autocorrect the names of the people on my team?” is a really good temperature check to see how much bias is built in because, if it does that, then you know that it goes deeper.
Christopher S. Penn – 14:41
So, what would you say is a good place to start to help companies maybe build in some practices for counteracting—let’s call it the low-hanging fruit, the easy, obvious bias—other than, “Don’t be a racist jerk, or don’t be a sexist jerk,” and things like that. I mean, those are human things, but in machines, what would you say would be a good place to start? Because maybe we should build something and give it away free to the community, like, “Hey, use this as part of your work.”
Katie Robbert – 15:18
I mean, it goes back to defining your 5P’s, quite honestly. You can’t get away from the 5P’s. It’s everywhere. But really making sure you’re clear on your purpose, but your purpose statement really needs to dig deep. It can’t just be, “We want to use AI to expedite.” What does that mean? What are you not willing to accept in terms of the results? And that’s when you go through the other four P’s, and that’s probably likely part of your performance. So, if my purpose is to use AI in a responsible way, my performance had better include things like: the output is not sexist, the output is not racist, the output is not misogynistic, the output is not harmful. But you need to define for the machine what that is.
Katie Robbert – 16:11
As far as I know, you can’t just say to the large language model, “Don’t be sexist,” because it isn’t going to necessarily know what that means to you. You need to define, as the human, what it means to not be sexist, to not be racist. You need to put those system instructions together to say, “This is what it means when I say you are not allowed to be the following things.”
Christopher S. Penn – 16:38
There is a role called sensitivity reader, right? This is a profession where these are people whose job it is to review manuscripts and texts and say, “Yeah, this could be problematic here. This is a major issue, or this could be misinterpreted this way. Perhaps don’t say it that way.” Here’s the good news. It is a well-defined profession. There’s a background to this, and there’s best practices. So, one of the things that we could do is use a language model to essentially ask it, “What does a sensitivity reader do? What does it look for?” So, I’m going to bring up a copy of Google Gemini here, 1.5, and I’ve asked it—I’ve used the Trust Insights PAIR framework, which is available at TrustInsights.ai/PAIR—and I’ve asked the questions like, “What does the sensitivity do? What are the best practices? What are the most common biases?” And from this, we’re going to take this very large amount of text that’s generated and say we’re going to build a scoring rubric. This is something we’ve covered on our live streams and things in the past, but the scoring rubric will go through and sort of build out the different things to look for. What kinds of things? Like slurs and derogatory stuff, cultural appropriation, et cetera. And then what you would do for any piece of content, anything that you wanted to validate, you would say, “Score this blog post with this—with our sensitivity reader rubric.” And if it comes up with anything less than a 90, let’s say the model—”You need to try again.” Or if it flags like, “This is probably sexist. This is probably racist. This is cultural appropriation.”
Christopher S. Penn – 18:31
You would then say to the model, “All right, take this piece, take this feedback, revise this piece to avoid doing that.” So, in the case where it was overtly making assumptions based on your name, you could take a model like this and say, “Okay, here’s—score this piece.” And it would—it should flag it. “Oh, that’s sexist. That is an assumption about your gender. That is—that is unfair.” And then you would say to the model, “Try again.” And I think this is a case where, in the 5P’s, the process has to involve this step to say, “Hey, check your work.”
Katie Robbert – 19:11
And you could also just hand it back to a person and say, “We scored this, and it’s over—it’s overtly sexist. You have to try again.” So, you can either have the machine rewrite it or a human rewrite it, depending on what your process is. But you’re absolutely right. We need to include in the process checking, QAing for these biases that we may not even be aware of. There’s a lot of—what do they call them?—microaggressions against people in the workplace, and those can show up in here. So, you need to be able to have a way—a neutral way—to check for those things versus—Chris, again, sort of terrible examples—but let’s say you decided to start going by your Korean name, and you start using the large language models, and then you hand it to me, and I have a bias against people of Korean ethnicity. I’m going to look at it as your boss and say, “It’s fine.” And as your boss, you’re going to have a hard time pushing back and saying, “It’s not fine. Look at all these things.” And I will say, “No, it’s fine. Let’s just put it out there. You need to move on and settle down and calm down.”
Christopher S. Penn – 20:34
And this is where I think AI can be an ally to many people to say, “Yeah, it’s not just me saying it. Here’s—with a good rubric and a good prompt and a willingness to include it in the process and say we want—” Going back to our framework, we want to reduce—we want to reduce the risk of the company of getting sued, losing customers. So, as part of the reducing risk—which means hopefully increasing profits because you reduce risk, increase profits—we want to implement this automated sensitivity detection in every piece of public content that goes out so that it is checked. It’s part of crisis communications in some ways, like, “Let’s prevent the crisis to begin with.”
Christopher S. Penn – 21:21
And so, I would—when you look at our framework—that is part of transparency and fairness. If we know what the biases are, and we do because we’re human, and we have implemented this part of the process in the 5P’s to say, “Here’s how we’re stopping or reducing these biases,” we can then truthfully say to people, “Here’s what we’re doing to try to prevent this.” Now, it’s not going to be perfect, but we can at least show a good faith effort that we are reducing the impact of these biases on our businesses.
Katie Robbert – 21:57
And that goes back to the first episode of AI responsibility and that transparency. And to your point, it’s not going to be perfect, you can’t please everyone, but as long as you can stand behind the work that you’re doing and say, “We tried our best, but also help us to get better”—to your audience, if you say, “Help us get better, help us learn, because we can’t know everything”— I can’t possibly understand the types of biases that you, Chris, experience, and vice versa. So, we have to learn from each other, we have to get better, and we have to then be accountable to include that. So that goes to the respect. We have respect for each other. We’re willing to learn, we have accountability. What we’re learning, we are then applying the fairness—
Katie Robbert – 22:50
Have we done things in a—back to the respectful way—and then transparency, demonstrating that we’re actually doing the thing. And that is how you start to break down that bias that exists in the workplace or just amongst people in general.
Christopher S. Penn – 23:13
Exactly right. Exactly right. So, we’re going to publish the rubric, a refined version of the rubric. We’ll put it in our Analytics for Marketers Slack group, and we’ll probably put it on our website at some point because this is one of those things where, yeah, we want to give this away. We want everyone to use this, to adopt it, to tune it to however you need so that we’re doing our part as a responsible user of AI, just as a— If you’ve got some ways that you have reduced bias or made good faith efforts to do so in your use of AI, stop by our free Slack group and tell us about it. Go to TrustInsights.ai/analyticsformarketers, where you and over 3,500 other marketers are asking and answering each other’s questions every single day.
Christopher S. Penn – 23:57
And wherever you watch or listen to the show—if there’s a challenge you’d rather have it on instead—go to TrustInsights.ai/tipodcast, where you can find us on all the places the best 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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