You Ask, I Answer: Liability for Marketing AI Models?

In today's episode, Mul uh Jessica asks who's liable for violations when it comes to marketing AI models or any artificial intelligence models. The company that hires the vendor or the vendor, anything in service agreements to look for. So artificial intelligence models, uh, it's just a really a fancy word for software. Right? At the end of the day, um, machines are writing software, but they're writing their own software.

But it's still software. It's still a piece of software, no different than you downloading a copy of, you know, Microsoft Word, um, or Excel or video game. And so the laws that apply to AI models largely are the same as the laws that apply to standard software, and frankly, the laws that apply to anything. Companies have some level of responsibility when it comes to uh product uh use and misuse, they have to warn you of things that are obviously um potentially dangerous or misleading. You know, when you look at your standard bottle of any kind of medication, it tells you, you know, when to use this product, directions to take it, um, don't take it for for these things.

So there's there's a little bit of responsibility here on the part of the company, but other things, you know, don't have those warnings necessarily because they're not really needed. Like you look at a jar of of you know, spicy peppers, there's no warning on here that says, you know, do not pour in your eyes, right? But people do crazy silly things like you know, eat Tide Pods. Um at the end of the day, when it comes to artificial intelligence, um, who's liable for the way that a model behaves is ultimately up to the end user. If you download a piece of pre-trained uh software, a pre-trained model from any vendor, and that's got a a bias in it of some kind, um, that is against a protected class and therefore not legal, you, the company and the user are responsible for what happens with that.

Right? You are responsible if you use it and then and this model causes your marketing uh or your operations or your customer service to discriminate against somebody on a protected class, you're responsible for your actions. You are responsible for it. Just the same as you know, if you go out and you buy a you know a a knife of some kind and you use it to cause somebody harm. It is not the knife's manu manufacturer's fault that you did something with the tool that was improper.

Um you, the user, were the person who did the bad thing and you are liable for it. Um and that can mean civil liability, like getting sued, or it can mean criminal liability like getting arrested. When you use any piece of software these days, actually, uh especially when it comes to downloading uh models and data from uh other people, uh other companies, uh, there's a good chance you signed an end user license agreement. Uh now, whether you read it in its entirety is not the company's problem. Um, but chances are somewhere buried deep within that end user license agreement is a uh statement that where you waived all liability and you assume entire responsibility, whole responsibility for what you do with the software.

Whether or not the software is flawed, whether or not the model is biased, whether or not the training data was any good to begin with or not, um, there's a good chance that you have uh incurred a hundred percent of the liability, especially if you're getting it from a a major commercial vendor like you know, Amazon or Google uh or IBM or anybody, um, there's a good chance that you are assuming a hundred percent of the risk. Now, if you are publishing data sets, if you are publishing uh pre-trained models, you definitely want to talk to your legal department to effectively do the same. Like, hey, we've done our best, and maybe you know, as as we were talking about, you list the ingredients of where you got the data, um, it's provenance and things, but you definitely want disclaimers of that uh as well. To if somebody downloads your data, your software, etc., uh, your models, you want to say, hey, we've done our best. We know there may still be issues in here, but you, the user, assume 100% of the liability for how you use this thing.

You cannot hold us liable. If you make a piece of software that discriminates uh discriminates against people who are uh you know transgender, guess what? That's not our fault. That's not our problem from a legal perspective. Um it is your problem as the user of the software.

Um it is your uh liability for you know you have to take ownership of your actions. In terms of service agreements, you've got to read them. You've got to read them and especially before you pay money to anybody, you've got to read them to understand what they are and are not accepting responsibility for. Um their documentation to the best extent that you can. If you're gonna be paying money to this vendor, you can ask that as part of your due diligence to say, give me your data lineage, give me your data provenance, give me your model governance, show me how you're keeping the model from drifting.

Show me uh any known issues. You know, just like when you buy a house, uh you have a house inspector and they come in and tell you, oh yeah, that looks like there was a leak there. Like it's old, but there's definitely a was a leak there, and you know that's out of out of code. You want to do that same level of due diligence with any models or software that you're working with to say to the vendor, hey, tell me what your known issues are. Uh disclose any uh material conditions that could cause issues.

And if the vendor says we don't have that documentation, sorry, we don't we don't we don't have that, um don't do business with them. The same as if they said, Oh, we know we our model's totally fine. There's absolutely no bias in it. So we don't have that documentation. Okay, that means you didn't do any checking.

Because almost every model and data set has some kind of bias in it, whether it's material or not, meaning has cons is consequential or not, is is uh secondary. But even in the the weekly newsletter I publish, the Trust Insights newsletter, whenever we publish a uh a study, we we disclose the methodology and say, like, you know, for example, with SEO, this is limited to the English language, so it automatically has a bias in it. Is that bias material? Maybe, maybe not. Um but if you every vendor who publishes any kind of data, models, algorithms, should be saying these are the known biases in here, um, take that with a grain of salt, right?

If if you feel like the data set uh needs to have more than just the English language in it for SEO, then our data would not be helpful to you, right? There's that disclosing of um known biases. That's the sign of a more mature vendor. They've they've got their documentation ordered, they've got their disclosures in order, um, they've told you what they know is wrong. And it's up to you to decide whether those things are important to you or not.

So, really good question on liability. At the end of the day, remember it comes down to the the tool manufacturer is not responsible for what you do with the tool. Um, you can do good things with it, you can do bad things with it, but at the end of the day, they're not liable for it. You are. So that's the rule to keep in mind.

Has any of this been settled in court? Not that I know of, but I also have not done a whole lot of checking on fine law. Um, but based on existing software law, based on existing general liability law, and what it and I'm not a lawyer, and disclosure for my own, not a lawyer, check with your own legal counsel. Um I don't think any of this has come up in court yet. I'm sure it will at some point, and uh it based on existing law, uh the courts are likely to rule uh that unless you were intentionally negligent or willful in the creation of your model, um that your the software manufacturer is probably not responsible for it.

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