You Ask, I Answer: Generative AI and Public Relations, Part 3 of 5

In today's episode, this is part three of five of an interview I did with Stephanie Moore of Kent State University about generative AI in public relations. Oh, that's great. Great examples. Okay, good. All right.

So talked we talked a lot about the benefits. Now let's go on to the challenges and limitations. So, what are the main challenges faced by PR professionals, communications professionals when integrating AI into practice? Knowing how to use the thing. Language models are good at language.

Ergo, they should be handed the language tasks. Non-language tasks should not be handed to language models. Like math, they're not particularly good at math. And neither are PR people, but um language models in particular, they can't count, right? That because that's not the way their architecture under the hood works.

And a lot of people, this is not just PR people, but a lot of people do not understand the mechanics of how these tools actually work under the hood. And therefore they kind of assume that either they're either they're people on one hand say it's just autocomplete, which is not true, or on the other hand, they say it's a magic, it's magic, which is also not true, right? Um that that is the primary limitation and challenge that everybody, including PR professionals, faces with AI, is not knowing how the technology works, not knowing what its limitations are, not knowing what a hallucination is and how do you reduce the likelihood of that happening, or which tools do you use? There's a great tool from Google called Notebook LM that is uh as close to zero hallucination as you can get because it will only read the data you provide it. So it doesn't know a thing.

Um you give it data and you say, tell me what's in here. So uh not to get too political, but there's this one document that people have been talking about um in the in the US presidential election that is put out by one of these parties. It's a 900-page document. I wanted to see what is being said in this document from the source itself, not relying on existing coverage and other people's opinions. So I put it in Notebook LM and I asked it a series of questions.

And I said, What does this document say about this? And it spits back on this page and this citation right here is what it says. And I look at it and go, wow, people weren't weren't lying. Like this thing is scary as hell. Um, but it returned truthful, cited, grounded um data.

So that's an you know another aspect of this. There, and there are, of course, you know, the the one of the big issues that PR firms are particularly need to be thinking about is from a legal perspective, when a machine creates something, it does not have copyright. This is true in all but five countries on the planet. Um there's a case in the United States called Naruto versus Slater, in which a chimpanzee took a selfie um and the photographer went to court. Uh it was a court case against PETA, and the court ruled, well, the chimp took the photo, not you.

Uh therefore the chimp did the work, therefore the chimp would technically hold the copyright, but chimps can't hold copyright, therefore the photo is in public domain because there is no copyright. That ruling has been extended to anything machine created, which is good and it should stay that way. Um, but knowing that um means that if you're publishing something on behalf of a client and you've used machines to generate it fully, meaning you didn't derive it from an existing work, then there is no copyright on that. And if your agency claw, if your agency contract has a line in it saying you assign copyright to the client, you're technically in breach of contract because you can't assign copyright to something that isn't copyrightable. That is so important.

Uh okay, good. So we talked a little bit about the legal concerns. What are some what what are some key ethical concerns related to using AI? What do you define as ethics? That's a great question.

Well, I I suppose you can look at the the codes of ethics within the field. You talked a little bit about billing, you know, in terms of there, I'm sure that there are some ethical implications associated with that and how you bill clients. Um if if you use a tool such as this and then take the work and pass it off on your as your own. I mean, those are ethical implications. So I see a lot of different ethical concerns with those are not AI ethics, though.

Those are human ethics. Those are human, those are human failings. Like if you're lying, you're lying. A machine can help you lie better, but you're still lying. Um there's three areas here, right?

So there's legal, right? You're what you are required to do by law. In the EU, for example, they just passed the EU AI Act, which, if you're familiar with GDPR, is probably going to be the gold standard for AI legislation around the world. There's a bunch of things provisions in that, but you have to comply with the law. There's there's no getting around that.

In ethics itself, there's two branches of ethics, right? There's deontology and consequentialism. Deontology says ethics is the rules are the rules, and the rules are inherently good. If you follow the rules, you are good. If you don't follow the rules, you are bad.

Consequentialism says the rules vary based on situation. What matters is the outcome. If you're doing more harm than good, you're bad. If you're doing more good than harm, you're good. And so these are the two major branches.

In AI, when it comes to the use of AI itself, all the tech companies by default have had to go a consequentialist route of let's avoid doing harm. Partly because you can't anticipate every possible situation, but also because deontology is inherently based in moral code. Um, and if so, if you are a Jew, you have a certain moral code. If you are a Muslim, you have a certain moral code. If you are a Buddhist, you have a certain moral code, they're all different.

Which one's right? We've been fighting wars for centuries, millennia, about who's right, and there's no answer. Um, so you can't build an uh a general purpose AI tool on deontological genealogical uh principles. It has to be consequentialist. And so when it comes to AI ethics, then the it is how can how can we help these models?

This is something that it's baked into every single model. Be helpful, harmless, and truthful. Now, the companies, when they build these tools, they heavily weight it towards harmless because that's what you can get sued for. Like, oh, this tool told me how to kill myself, right? Okay, let's say it doesn't allow, we're gonna say it won't allow you to do that.

So it will sacrifice helpful and it will sacrifice truthful to be harmless. If harmless is off the table, it will prioritize helpful over truthful. That's how you get hallucinations. Um, hallucinations are essentially when if you think about the model's knowledge as a library. Some models are really big, they have really big libraries, some models are really small libraries.

And the model tries to go into the library and pull a book off the shelf that answers your question. It wants to be helpful, but it may not have the exact book you're looking for. So you say, I want the joy of cooking, the model goes into the library and says, Don't see that. I'll grab the closest one here. This is the joy of sex.

That's what you wanted, right? You're like, no, they're not the same at all. Well, it's close, it's close. Um, that's how hallucinations happen. And so helpful, harmless, truthful is a general good rubric for consequentialism, but it has trade-offs.

And those trade-offs are it's going to be harmless first, it's going to be helpful next, and it'll be truthful last. That obviously has bigger implications on things like the media landscape, because truthful, depending on your political persuasion, may or may not be really important, right? Um, depending on whether you get paid as a journalist on page views and clicks. Um, we have a capitalist ecosystem that prioritizes performance. And so as a journalist, you will say things that may, depending on your publication and your own personal ethics, may or may not be true, just so that you can get a paycheck.

Um, again, these are not AI problems, these are human problems. However, AI is an amplifier. This is a paraphrase from Captain America. It takes the good and makes it great, it takes the bad and makes it worse. It makes everything more of itself.

It's like salt, right? You put it on a food, it makes it taste more like the food. And as a result, if you are if you if AI is in the hands of someone who is ethically problematic, it's gonna make them worse. That makes sense. If you enjoyed this video, please hit the like button.

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