So, Herman, I was thinking this morning about how we often treat artificial intelligence like it is this objective, digital oracle. You know, you ask it a question, and it gives you an answer, and there is this underlying assumption that the response is coming from a place of pure, unadulterated logic. But our housemate Daniel sent us a prompt today that really pulls back the curtain on that illusion. He is asking about the cultural fingerprints we leave on these models during their creation. It is February seventeenth, twenty-twenty-six, and we are still grappling with the fact that these machines are essentially mirrors of our own messy, biased selves.
Herman Poppleberry here, and I have to say, Daniel really hit on a nerve with this one. It is a topic we have touched on in bits and pieces over the last six hundred-some-odd episodes, but looking at them together—training data versus post-training reinforcement—is where the real magic, or the real mess, happens. It is like asking if a person is more shaped by the books they read in a library or by the specific teachers who graded their papers and told them which ideas were "correct." Both are massive influences, but they function in very different ways. And in the context of twenty-twenty-six, where we are seeing models like G P T five and Claude four being integrated into everything from legal advice to medical diagnostics, the stakes for this "cultural transfer" have never been higher.
Exactly. And for those just joining us for episode six hundred fifty-four of My Weird Prompts, we are diving deep into the architecture of bias. We have talked about training data as the foundation and reinforcement learning as the steering wheel. But Daniel’s question is about potency. Which one actually has more power to bake a specific worldview into an artificial intelligence? Is it the trillions of words it "reads" during its infancy, or the specific "lessons" it is taught by human trainers right before it is released to the public?
It is a fascinating debate in the research community right now. If you look at the sheer volume, training data wins by a landslide. We are talking about trillions of tokens of text. This is the Common Crawl, which is basically a massive scrape of the entire public internet. It includes Wikipedia, Reddit, news sites, digitized books, and scientific papers. If the internet has a bias—and let us be honest, it has many—the model is going to inhale all of it. If you spend your whole life reading only one type of literature, you are going to develop a very specific way of seeing the world. That is the base model. It is the "Id" of the A I, containing everything from the most brilliant scientific breakthroughs to the darkest corners of internet forums.
Right, but then you have the post-training phase. This is what most people actually interact with when they use a chatbot. The base model is often quite raw, unpredictable, and even chaotic. It might complete a sentence in a way that is factually correct but socially unacceptable. It is the Reinforcement Learning from Human Feedback, or R L H F, that turns it into a helpful, polite assistant. And that process involves humans—actual people—ranking responses. They say, "this answer is better than that one because it is more polite," or "this one is more aligned with our safety guidelines." So, Herman, in your view, which one is the more potent carrier of cultural bias?
I would argue that while training data is the source of the raw material, Reinforcement Learning from Human Feedback is the more potent form of intentional cultural transfer. Think of it this way: the training data is like the ocean. It is vast, it is messy, and it contains everything from beautiful coral reefs to plastic waste. But the reinforcement learning is the filter we put on the tap. It determines what actually comes out when you turn the handle. Researchers at Stanford and M I T have published several papers over the last two years showing that you can take a model trained on the same massive dataset and, through a relatively small amount of reinforcement learning—sometimes just a few thousand high-quality examples—give it a completely different political or cultural "personality." You can make it lean progressive, conservative, or even libertarian, just by changing who is doing the ranking.
That is a great analogy. It reminds me of that study from twenty-twenty-four where they took a base model and used different sets of human labelers to fine-tune it. When they used labelers who identified as more progressive, the model’s outputs on social issues shifted significantly. When they used a different group, the model shifted the other way. Even though the "knowledge" in the model stayed the same, the "voice" changed. But doesn't the training data still set the boundaries of what is possible? If a concept isn't in the training data, the model can't talk about it, no matter how much reinforcement you do.
Absolutely. That is what we call the "knowledge bottleneck." If the training data is ninety percent English and heavily weighted toward North American and Western European perspectives—which, despite efforts to diversify, it still largely is—the model is naturally going to be U S-centric. It will know more about the Super Bowl than it does about the nuances of local governance in Southeast Asia or the oral traditions of West Africa. That is a systemic bias that is very hard to "fix" in post-training because you are trying to steer a ship that doesn't even have a map for those other regions. You can tell a model to be "inclusive," but if it doesn't have the underlying data to understand the context of a specific culture, its "inclusivity" will feel performative or shallow.
So, we have this two-layered problem. You have the systemic bias of the internet's dominant cultures in the data, and then you have the specific, often Silicon Valley-centric values of the people doing the reinforcement learning. Daniel asked how we can mitigate this, especially for users from diverse backgrounds. He wants to know if we can keep a model "vanilla" or neutral until the user applies their own prompt. Is that even technically possible, Herman? Or is "neutral" just another word for "the developer's bias"?
That is the million-dollar question. Truly "neutral" might be a myth because every language carries cultural baggage. Even the way we structure a sentence in English carries certain logical assumptions about time, agency, and hierarchy. However, there are some really interesting technical approaches being explored in twenty-twenty-six. One is called Constitutional A I, which was pioneered by Anthropic but has since been adopted and expanded by others. Instead of just having humans rank things based on their own vibes, they give the model a written "constitution"—a set of explicit principles like "be non-judgmental" or "avoid Western-centric assumptions." Then the model uses those principles to evaluate its own responses through a process called R L A I F—Reinforcement Learning from A I Feedback.
I like the idea of a constitution because at least it is transparent. You can read the document and see what the values are. It is much better than a "black box" of anonymous gig workers ranking things. But it still raises the question of who writes the constitution. If I am a user in Jerusalem, my "neutral" might look very different from a user in Tokyo or Lagos. How do we make sure a model doesn't become a "stochastic parrot" for just one culture's version of morality?
One way is through something called "pluralistic alignment." Instead of trying to find one single "correct" way for the model to behave, researchers are looking at ways to train models on multiple different value sets simultaneously. Imagine a model that has a "toggle" or can detect the cultural context of the user. If you are asking about a sensitive historical event, instead of giving one "neutral" answer that satisfies no one, it could say, "From this cultural perspective, the event is viewed this way, while from this other perspective, it is viewed that way." It moves from being an arbiter of truth to being a map of different human perspectives. This is what the "Collective Constitutional A I" projects are trying to achieve—crowdsourcing the "rules" from thousands of people across the globe rather than just a few dozen engineers in California.
That feels much more honest. It reminds me of how we try to approach things on this show. We aren't trying to give the one final answer; we are trying to explore the space. But let's talk about the user's role. Daniel mentioned the user's prompt as the final layer. If we have a truly "vanilla" model, the system prompt—those instructions you give the A I before you start the conversation—becomes the primary driver. We are seeing more companies allow users to set "Custom Instructions." Do you think that is the ultimate solution? Just let the user decide their own bias?
It is a powerful tool, but it has a limit. If the model has been "over-steered" during the reinforcement learning phase, it might suffer from what we call "alignment tax." This is when the model becomes so focused on being polite or following its safety guidelines that it actually becomes less capable or refuses to answer perfectly valid questions. If the reinforcement learning is too heavy-handed, your custom prompt might not be enough to break through that "preachy" layer. We have all seen those moments where an A I gives you a lecture on why it can't answer a question instead of just answering it. That is the R L H F overriding the user's intent.
Oh, I've definitely been there. It feels like talking to a very H R-compliant robot that is terrified of saying anything remotely controversial. It is frustrating because it often shuts down genuine intellectual inquiry. If I'm trying to understand the arguments for a specific, perhaps unpopular, philosophical position, I want the A I to lay them out clearly, not tell me why those arguments might be problematic before I've even heard them. It feels like the "Superego" of the model is constantly shushing the "Ego."
Exactly. And that brings us to the research side of Daniel's question. Which of these areas has been more explored? Historically, training data bias has a much longer paper trail. We have been talking about "garbage in, garbage out" for decades in computer science. There is legendary work by people like Timnit Gebru and Margaret Mitchell on the dangers of large language models and the biases inherent in massive datasets. Their twenty-twenty-one paper, "On the Dangers of Stochastic Parrots," was a watershed moment. It forced the industry to look at how things like gender and racial bias were being baked into word embeddings at the most fundamental level.
Right, I remember that paper. It was a huge wake-up call. It showed how a model would associate "doctor" with "man" and "nurse" with "woman" simply because that was the statistical reality of the text it was trained on. It wasn't "thinking"; it was just reflecting the historical biases of our society.
Precisely. That research is well-established. But the research into R L H F bias—the post-training stuff—is the new frontier. It is much more recent because the technology itself only became the industry standard around twenty-twenty-two or twenty-twenty-three. Now, in twenty-twenty-six, we are seeing a flood of papers analyzing the "political compass" of different models. There was a significant study by researchers at the University of Washington that showed how different fine-tuning methods can push a model toward different ends of the political spectrum. They are finding that the "human" in "human feedback" is a very specific kind of human—often a gig worker in a developing country following a rubric written by a researcher in a high-income country. This creates a "double-layered" bias: the bias of the rubric-writer and the bias of the person interpreting that rubric.
That is such a fascinating layer of complexity. We think of it as "A I bias," but it is really a chain of human biases. From the person who wrote a random blog post in two thousand twelve, to the researcher who wrote the labeling guidelines in twenty-twenty-four, to the person in Kenya or the Philippines who is actually doing the labeling today. It is a global game of telephone, but with consequences for how we understand truth.
It really is. And the companies are starting to realize that they can't just have one "global" model that works for everyone. OpenAI published their "Model Spec" back in twenty-twenty-four, which was an attempt to be more transparent about how they want their models to behave. It was a massive document that outlined the rules for the model. But even that is a form of bias transfer. They are choosing what "good" looks like. In twenty-twenty-five, we saw the rise of "Sovereign A I," where different countries started building their own models trained on their own national datasets and aligned with their own cultural values. France has one, India has several, and we are seeing a real fragmentation of the A I landscape.
So, if we are looking at the future, what is the path forward for mitigation? If I'm a developer or even just a power user, how do I navigate this?
I think the first step is moving away from the idea of "de-biasing." You can't actually remove bias from a language model because language itself is a system of biases and cultural shortcuts. Instead, we should be talking about "bias awareness" and "multi-cultural alignment." We need tools that allow us to see the "flavor" of a model before we use it. Imagine if every A I model came with a "nutrition label" that told you: "This model is sixty percent based on Western English data, its reinforcement learning was done by this specific demographic, and its safety guidelines prioritize these three specific values."
That would be incredible. It puts the power back in the hands of the user. Instead of being told "this is the objective truth," you are being told "this is the perspective this model was built to reflect." It changes the relationship from one of blind trust to one of informed collaboration. I can choose the "French-aligned" model for a question about art history and perhaps a "Singaporean-aligned" model for a question about urban planning.
And we are seeing some movements toward that. There are open-source projects like the "Dolly" dataset or "Open Assistant" where the training and reinforcement data are completely public. You can actually go in and see the specific conversations that were used to train the model. That level of transparency is the only real antidote to the "invisible" bias Daniel is talking about. If you can see the "teacher's notes," you can understand why the "student" is answering the way it is.
I think there is also a role for what I’d call "adversarial curiosity." As users, we should be testing the edges of these models. If you feel like a model is giving you a very one-sided answer, ask it to take the opposite perspective. Use your prompts to force it out of its "Silicon Valley bubble." It is amazing how much a good prompt can bypass some of those post-training guardrails if you are clever about it. You can say, "Argue for this position as if you were a nineteenth-century philosopher from Kyoto," and suddenly the R L H F "politeness" layer might peel back to reveal something more interesting.
True, but that shouldn't be the user's job by default. The burden should be on the creators to provide a more representative foundation. One of the most interesting areas of research right now is "data curation." Instead of just scraping the whole internet—which is full of "noise" and "toxic" content—researchers are being much more selective. They are looking for high-quality, diverse data sources that represent a wider range of human experience. It is more expensive and it takes longer, but the resulting models are often much more robust and less prone to those "stochastic parrot" traps. We are moving from a "Big Data" era to a "Good Data" era.
It’s like moving from a fast-food diet of random internet comments to a balanced diet of carefully selected literature and diverse perspectives. It makes sense that the output would be healthier. But Herman, let’s get back to Daniel’s question about which area is more explored. If a student or a researcher wanted to make a mark in this field today, where is the most "unmapped" territory?
I would say the unmapped territory is in "cross-cultural R L H F." Most of the reinforcement learning research has been done in English, by English speakers, for English-speaking markets. We have very little data on how reinforcement learning works across different languages and cultural norms. For example, the concept of "politeness" in Japanese is fundamentally different from "politeness" in American English. If you apply a U S-centric reinforcement learning rubric to a Japanese language model, you are going to get something that feels "off" or even disrespectful to a native speaker. Mapping those cultural nuances in the reinforcement learning phase is a huge, wide-open field. We need "cultural translators" who are also A I researchers.
That is such a good point. We are essentially exporting a specific brand of digital etiquette to the rest of the world. It’s a new kind of cultural imperialism, but happening through R L H F. If we want A I to be a truly global tool, it has to be able to "code-switch" culturally, not just linguistically. It needs to understand that "neutrality" in one country might be "complicity" in another.
Exactly. And that brings us to the practical takeaways for our listeners. Because this stuff can feel very abstract, but it affects how we use these tools every day. First, realize that whenever you are using an A I, you are participating in a cultural exchange. You aren't just talking to a machine; you are talking to the collective shadow of the internet and a specific group of researchers' ideas of what is "good."
That is a great way to put it. The "collective shadow." And my takeaway would be to embrace the "Custom Instructions" or system prompts. Don't just take the default setting. If you want a model that is more analytical and less "preachy," tell it that. If you want it to prioritize a specific cultural context, make that clear. You have more power than you think to shape the "voice" of the A I you are working with. You are the final "fine-tuner."
And for the developers listening, the lesson is transparency. If you are building or fine-tuning a model, document your choices. Tell us who your labelers were. Tell us what your "constitution" looks like. The more we know about the "who" and the "how," the better we can understand the "what." In twenty-twenty-six, transparency is the new performance metric. It doesn't matter how fast your model is if we don't know whose values it is promoting.
I think we've covered a lot of ground here. From the massive, messy foundation of the Common Crawl to the polished, sometimes overly-guarded results of R L H F. It is clear that while training data provides the scope, reinforcement learning provides the direction. And both are deeply human processes, for better or worse. We are essentially teaching these machines how to be "us," but we haven't quite decided which "us" we want them to be.
It’s a journey, Corn. We are still in the early days of understanding how these digital minds are shaped. But the more we talk about it, the less "weird" and the more "human" it becomes. Daniel, thanks for sending that one in. It really made us dig into the guts of the system today. It is a reminder that there is no such thing as a "view from nowhere." Every A I has a home, a history, and a set of parents.
Definitely. And hey, if you have been enjoying these deep dives into the world of A I and beyond, we would really appreciate it if you could leave us a review on your podcast app or on Spotify. It genuinely helps other curious minds find the show. We are aiming to hit one thousand reviews by the end of the year, and we are getting close!
It really does help. We love seeing the community grow and hearing your perspectives. You can find all our past episodes and a way to get in touch with us at our website, myweirdprompts.com. We have the full R S S feed there too, so you never miss a prompt.
This has been My Weird Prompts. I'm Corn.
And I'm Herman Poppleberry. Thanks for listening, and we will talk to you in the next one.
Goodbye, everyone!
See ya!
So, Herman, I was thinking about the "stochastic parrot" thing again. If the model is just repeating patterns, does that mean it can never truly be "creative" in a way that transcends its bias? Or is creativity itself just a very sophisticated form of bias-mixing?
That is a whole other episode, my brother. But short answer? I think creativity often comes from the friction between different biases. If you can get a model to reconcile two opposing worldviews, that is where the new ideas happen. It’s the synthesis of the thesis and the antithesis.
Friction as a source of light. I like that. Alright, let's go get some coffee. I think Daniel is already in the kitchen, probably writing his next prompt.
Lead the way. I need some caffeine to process all this "pluralistic alignment" talk.
Before we go, I just wanted to mention one more thing about the research. I saw a paper recently from the University of Oxford that was looking at "moral uncertainty" in A I. Instead of the model being "sure" about a biased answer, they are trying to train it to express doubt. Like, "I'm eighty percent sure this is the consensus view, but there is a twenty percent alternative here."
Oh, that is brilliant. Quantification of uncertainty is the ultimate humility for an A I. If it can tell you it doesn't know for sure, it's already more "neutral" than most humans. We tend to be very confident in our biases; an A I that admits its own limitations is a huge step forward.
Exactly. It moves us away from the "oracle" model and toward a "consultant" model. "Here is the data, here is the bias, make of it what you will." It respects the user's intelligence.
I love that. Maybe that's the future. Not a neutral A I, but an honest one. An A I that knows it has a perspective and isn't afraid to show its work.
An honest A I. Now that would be a weird prompt.
Let's save that for next time. I can already feel a deep dive coming on.
Deal. Thanks for listening, everyone. This has been My Weird Prompts. We're on Spotify and at myweirdprompts.com. See you next time.
Bye!
Herman, wait, did we actually answer Daniel's question about which one is more explored? I feel like we pivoted to the future of research pretty quickly.
Good catch. Let's be precise for Daniel. Training data bias has definitely been more explored in terms of academic papers over the last decade. There are thousands of studies on datasets like ImageNet or the various text corpora. But if you look at the last twenty-four months, the "heat" is almost entirely on R L H F and alignment. That is where all the venture capital and the top-tier lab research is going right now. So, it depends on how you define "more explored"—by volume over time, it's training data; by current intensity and industry focus, it's R L H F.
That makes sense. The old guard is the data, the new guard is the alignment. It’s like the difference between studying the geology of a mountain and studying the people who are building a resort on top of it. The geology has been studied for a hundred years, but everyone is currently talking about the resort.
Perfectly put. The geology is the data; the resort is the R L H F. And we all know which one gets more attention in the brochures.
Haha, true. Alright, now we are actually going. I can smell the coffee from here.
For real this time. Bye!
Bye!
Wait, Corn, one more thing... do you think the coffee machine has its own internal bias for dark roast?
No, Herman! Coffee! Now!
Fine, fine. Coffee it is.
Thanks again for listening to My Weird Prompts. Check out myweirdprompts.com for more.
And leave that review! It really helps us keep the lights on.
Okay, now we're done. Goodbye!
Goodbye!
You know, I was just thinking... if the training data is the "collective shadow," then the R L H F is the "superego." It’s the part that tells the model how to behave in polite society, while the training data is the "Id" full of all those raw, unfiltered human impulses.
That is a very Freudian take on machine learning, Corn. I love it. The "Id" is the raw internet data, the "Superego" is the R L H F, and the "Ego" is the final response we see on the screen.
Exactly! And just like in people, the Ego is often caught in a struggle between the two. Sometimes the Id slips through, and sometimes the Superego is too repressive.
We should write a paper on "Digital Psychoanalysis." We could be the first practitioners in the field.
Let's just start with the coffee. One step at a time, Herman.
Right. Coffee. The ultimate ego-booster.
This has been My Weird Prompts. See you next time on Spotify and at myweirdprompts.com.
Bye!
