LLM Ethics and Bias
Let’s play word association. When someone says “language model,” do you think of convenience, autocomplete, or the thing helping you write that email you’ve been avoiding for two days? Whatever your gut instinct, you probably don’t immediately think: values. But that’s exactly the question we should be askingdo language models have values… and if so, who put them there, and what does it mean for the rest of us?
Oops, My Algorithm Is Showing
Before we dive deep, let’s detach from the techno-speak for a second. Bias isn’t some mysterious glitch in the matrix; it’s a reflection of usour debates, our histories, our dogmas, and yes, our memes. When models are trained on gigantic swaths of internet data, they absorb more than just grammar and punctuation. They learn patterns, perspectives, preferences. In short, they’re learning to speak… like usand that includes picking up our ethical laundry, clean or otherwise.
So what happens when that speech starts sounding like it’s taking sides? Or worse, when it’s feeding back your own biases in a neatly wrapped, hyper-confident tone? Welcome to the modern spaghetti junction of ethics in large language models, where right, wrong, and “statistically probable” often collide.
The Myth of the Neutral Machine
Let’s bust a prevailing myth: technology is not value-neutral. A calculator might not care whether it’s adding up kindergarten jellybeans or dark money campaign fundsbut language is different. Every model, by virtue of its training data and developers, carries assumptions about the world. These assumptionsoften imperceptible at first glancecreep into outputs through word choice, tone, and the subtle nudge of what’s said… and what’s omitted.
“Every large language model is a reflectiondistorted or otherwiseof the society it’s trained on.”
So when a system answers a question about politics, gender, or social justice with remarkable fluency, the real question isn’t just “is it right?” It’s “whose right is it modeling?”
The Tug-of-War Over Values
In the HBR piece that inspired this article, the researchers asked whether these systems “have” values, and more importantly, whose values they represent. What they found was fascinating: different models reflected different moral preferences depending on who trained them, where, and with what philosophies software engineers consciously or unconsciously instilled throughout development.
Some models leaned individualistic, others leaned collectivist. Some prioritized care ethics over principles of justice. If all this sounds like a philosophy class you didn’t sign up for, brace yourselfthe values embedded in these tools increasingly steer everything from hiring decisions to healthcare recommendations. This isn’t theoretical anymore. It’s infrastructural.
Bias Is Not a Bug, It’s a Mirror
There’s a tendency to view bias in tech as a fixable problem. Like a software patch we can deploy once a new issue surfaces in the headlines. But bias isn’t always a mistake. Sometimes it’s simply an accurate reflection of the data we fed the machine. This is what makes the conversation around “ethical alignment” so importantand so sticky.
Let’s be clear: Aligning outputs with specific ethical norms is not just about reducing harm. It’s also about deciding who gets to define harm in the first place. And that brings us back to an age-old tension: do we want our utilities to be neutral? Or do we want them to guide, protect, and maybe even challenge us ethically?
Programmable Morality? Handle with Care
There’s been talk lately about “constitutional AI” (yes, we said we’d avoid that word, but we’re journalists, we investigate contradictory things). Essentially: a framework of rules meant to help models avoid unethical behavior. Sounds promising… until you realize ethics is less like a universal constitution and more like a constantly shifting sand dune on fire.
What’s ethical in one country might be problematic elsewhere. A model that’s too conservative in moderation might suppress valuable dissent. One too liberal might amplify toxicity. Just try to program that into your next update.
So What Do We Do?
First: transparency. If these models reflect chosen ethical frameworks, let’s make that explicit. We deserve to know the moral compass behind the assistants influencing hiring platforms, search results, and school curricula.
Second: diversity in development. If we’re outsourcing moral reasoning to silicon scribes, the teams shaping them should look a lot more like the users they impactculturally, ideologically, and experientially.
Third: humility. These tools are impressive. But we’d do well to remember: being fluent doesn’t mean being wise. Models can impersonate perspectives, but they don’t live with the weight of consequences. We do.
The Final Word (For Now)
In a way, all of this returns us to a deeply human question we’ve been grappling with since Plato: how do we build toolswhether fire or language modelsthat serve and not scorch us? That question isn’t going away, and the tools aren’t slowing down. If we’re going to invite these bots into our inboxes, classrooms, and courtrooms, we need to be a whole lot more intentional about what values we’re packaging with themnot because they’re perfect, but because they’re persuasive.
So do large language models have values? In a word: yes. But they’re not their own. They’re ours. Which means if we want better outcomes, it’s not the code that needs rewritingit’s the conversation.
