What AI Gets Wrong When It Plays a Personality

Researchers used my Light Triad scale to test whether a machine can simulate moral character. What they found says something hopeful about being human.

Every few months I get a version of the same question: now that AI can sound like anyone, can it be anyone? Can a large language model take on a personality the way a good actor does, and reason the way that kind of person actually reasons?

A new paper out this month in Ethics & Behavior put that question to the test, and it did so on territory I care about. A team led by Nuermaimaiti Wubuli compared how dark and light personality traits shape moral judgment in two very different kinds of minds: 404 human participants, and 2,092 responses generated by large language models (GPT-4.1 and DeepSeek).

To measure the bright side of human nature, they used a scale I built with my colleagues back in 2019, the Light Triad — humanism, Kantianism, and faith in humanity — the prosocial counterweight to the Dark Triad of narcissism, Machiavellianism, and psychopathy (plus, in more recent work, sadism).

They scored everyone’s moral choices using the CNI model, a clever tool that pulls apart three things people are usually doing all at once in a moral dilemma: their sensitivity to consequences, their sensitivity to moral norms, and their general preference for inaction over action.

Here is the first finding, and I will admit it was nice to see. In the human sample, the pattern held the way the theory predicts. People higher in dark traits were less sensitive to moral norms. People higher in light traits were more sensitive to them. Machiavellianism and sadism pulled norm-sensitivity down; Kantianism pulled it up. And people high in faith in humanity were more willing to act when a dilemma landed in front of them, which makes a lot of sense: if you trust that people are basically good, you are more likely to step in. This was a sample collected in China, on the other side of the world from where the scale was built, and the structure mostly carried.

But the part I keep thinking about is the AI.

When the researchers prompted the models to “be” a high-Machiavellian, or a deep narcissist, or a profoundly humane person, and then run the same dilemmas, something revealing happened: The machines matched only the direction of the human pattern — dark prompt, lower norm-sensitivity; light prompt, higher — and missed almost everything underneath it. Their dark characters didn’t just score low on moral norms. They scored close to zero, in a flat, absolute way that no actual human being does. The responses were rigid, extreme, and cartoonish.

And here is the kicker: The models couldn’t tell the dark traits apart. Machiavellianism, narcissism, psychopathy, and sadism are four distinct constructs with four distinct inner logics, and they all came out as basically the same response pattern. Same story for the light traits. The researchers handed the models careful background on what makes each trait different, and the models still collapsed them into one generic “bad person” and one generic “good person.” Their own conclusion: today’s LLMs show “only a preliminary simulation capacity” and “fail to reflect the complex features of personality and moral judgment.”

Now, the lazy version of this take is “AI bad, humans good,” and that is not what I think. The models got the direction right, which is genuinely useful. As the authors point out, that makes them a decent tool for a rough first pass, a way to guess which way an effect might lean before you go collect real data. Yes, and: a rough first pass is not a person.

What the machine reproduced was the stereotype of a personality. What it could not reproduce was the texture. A real Machiavellian and a real narcissist walk into the same moral dilemma and come out at different places. A genuinely humane person sometimes breaks a rule precisely because they care about a human in front of them. An actual person integrates this specific situation with a lifetime of accumulated experience, and the result is particular in a way that resists averaging. The models had read everything ever written about narcissism, and they still couldn’t do narcissism — because doing it takes something other than having read about it.

I have been reading a lot lately about how so much AI-generated prose feels empty even when it is perfectly fluent, and this study points at that same emptiness from another direction. A language model hands you the average of what a “cruel person” or a “passionate person” sounds like. The thing it overlooks is the only thing that was ever interesting in the first place: the particular human, with their particular history, doing the unaverageable thing in a particularly morally-challenging situation.

I don’t take this as a warning about machines so much as a reminder of what we are. Your conscience is the running, lived, context-soaked work of a single irreplaceable life, and at least for now, the most sophisticated models on earth keep quietly averaging that part away.

Which is worth thinking about. The most human thing about your moral life may turn out to be exactly the thing no one has figured out how to fake.