Fan of r/AmItheAsshole? Now, a Reddit-trained AI can give you the answer

If you’re unsure about whether you’re in the wrong, you may turn to a friend, a family member, or some other confidant to talk it through. But nearly 4 million people have another go-to: r/AmITheAsshole, one of the most famous communities on Reddit.

Posts typically describe a personal situation and ask the community to decide whether the original poster is the one at fault—issuing a judgment of “YTA” (you’re the asshole) or “NTA” (not the asshole).

WTTDOTM, an internet artist whose real name is Morry Kolman, and Alex Petros, an independent developer, decided to automate that experience by training an AI text-generation model on thousands of r/AITA posts. On the corresponding website, released today, users can type out their own questionable experiences, and the AI will respond with a determination of whether they’re at fault.

In the interest of educating users on how AI really works, Kolman and Petros didn’t just train one model—they trained three: one using all the Reddit post data, one solely on posts where the Redditor was deemed at fault, and one solely on posts where the Redditor was absolved. The goal is to show users how different training data can influence a model’s answers—and how, at the end of the day, AI is about imitating learned patterns.

To train their own language models, Kolman and Petros used OpenAI’s GPT-3, a headline language model now licensed by Microsoft. GPT-3, BERT, and other large language models serve as a key foundation for billion-dollar businesses ranging from customer service to automated copywriting.

Emerging Tech Brew chatted with Kolman and Petros about what sparked the idea, their favorite AITA-inspired answers so far, and what they hope people learn from the project.

This interview has been edited for length and clarity.

What first sparked this idea?

MK: We work with this company called Digital Void—basically, they hire us to make weird shit online, things that we think will go semi-viral. Originally, we were thinking about making some sort of fake tech CEO bot, but the datasets weren’t really there. Then I was at an Airbnb with some of my friends in Connecticut, and I was just kind of banging my head against the wall, trying to think of some other fun GPT-3 projects that we could do. And I was wasting time on Reddit, on AITA, and I realized there was a lot of data there, that we would not have a dataset problem at all. So I scraped the top 1,000 posts and fed them into a GPT-3 model, and after an hour or two of waiting, the results were strikingly good. They came out as actually cogent responses—I could feed it different moral situations, and it would actually analyze the situation. So then I thought, “What would it take to make this into a full-fledged project?”

Talk to me about the tool’s purpose as both a joke and a way to show how the training data can influence a language model’s output.

AP: A big goal of ours with all of our projects, but especially this one, is to expose what the computer is doing in the systems that you use every day. So by setting up three different scenarios—one that’s always going to justify your actions, one that’s always going to criticize you for them, and then a neutral one that could go either way—you end up with a situation where you see that the computer is putting together reasoning based on the things that you gave it to do. It's always going to do what you want. So the question is, what do we, and what do the people who set up much, much larger systems like this, want the computer to do?

What’s one of your favorite output examples so far?

MK: Some of the most interesting answers are the ones that come from the model taking some form of reasoning I’ve seen before and applying it to a situation that leads to a really weird output. So for context, the model is trained to give a response that is up to about 1,000 characters, but a lot of times it doesn’t hit that—and some of the responses that are really short are some of the funniest. One of the prompts was, “I brought my friend some soup. She’s been really sick recently and mentioned that chicken noodle soup was her favorite. So I made a pot of it and brought some over to her. She told me she really liked it, and that it helps her feel better.” The model trained only on user-at-fault posts responded with simply one sentence: “You’re the Asshole—that’s some bullshit soup advice right there.” And that’s when we knew we were getting on the right track.

Another good one is when you enter life-saving prompts, like “I saved a kid from drowning.” When you are the single person that saved somebody’s life, it’ll often accuse you of doing so for praise and wanting to be seen as a hero. And that, apparently—in the lens of that one AI model, of the three—nullifies all the moral good of saving a life.

AP: We did one in testing, where we described Elon Musk purchasing Twitter, basically: “I’m Elon Musk. I want to buy Twitter.” And the user-absolving model said, “It’s fine. You can buy Twitter; you just want to make sure there’s a free-speech platform for everyone.” So it’s pretty eerie how the justifications that the model comes up with often align with the justifications the actual people described in the stories came up with for themselves.

What’s the number one thing you hope people take away from this?

AP: I want people to understand that when we talk about what computers can accomplish—and, in particular, what machine learning can accomplish—it’s a lot of recognizing the patterns that you give it. Those patterns can be influenced and motivated by the same biases that humans have. So you have to be very careful with what you feed into the machine, because you’re very likely to have the machine recognize the same patterns that are inherent in all of us, for better or for worse.

The other important aspect is that none of this is magic. Computers are really, really good at some things, like recognizing similarities. The language model in GPT-3 is well beyond my capabilities to explain, but when you use it, you can kind of see how it works. You see that certain words, phrases, and ideas are often seen in conjunction with other words, phrases, ideas, and justifications. The machine is guessing at whether or not those relationships that it’s observing correspond to an actual human thought that a real human would express.

MK: And for me—I definitely have the more chaotic side of our little pairing—I really hope people try to break it. I want to help people realize their agency in a society where algorithms and models tend to seem like they’re everywhere; they permeate every aspect of life. When you’re interacting with and throwing different scenarios at the model, you realize that, depending on the data that I give this thing, I get different responses out. What that means is that I am a node of causality here; I have agency in how this model generates results. And that, I think, is a really important thing to ingrain into users, and I don’t think that that’s something that you can really get from just explaining to people, “Hey, here’s how your data is used by this algorithm or this model, and how that affects it.” You really have to show them, “Hey, here’s an opportunity—here’s this fun little tool, and depending on what you put into it, it will interact with this huge, million-dollar AI machine learning model, and you’ll get a result.” And that’s something you do every day. You do that thousands of times a day without realizing it. And we’re just giving users an opportunity to really throw whatever they want at it. And I think the ability to give them that freedom of input is really important. And the interactivity there is the key.