Aristotle didn’t write code, but he did leave us a debugging tip for human affairs. Virtue lives in the mean, not the extremes. That old idea: Avoid the vices that lurk at either edge turns out to also be a surprisingly good recipe for modern artificial intelligence, or AI.
In the School of Computing and Augmented Intelligence, part of the Ira A. Fulton Schools of Engineering at Arizona State University, Hasan Davulcu and collaborators are building systems that can find the mean, explain how they got there, and stay there — on purpose.
“AI has to be controllable, programmable, and most importantly, understandable,” says Davulcu, a Fulton Schools professor of computer science and engineering. “That’s what I call the golden mean of AI. It’s an approach that looks at human experience, finds the middle ground and holds to it.”
Why “big AI” gets pulled to the extremes
Large language models, or LLMs, the kind of AI that powers tools like ChatGPT, are trained by scanning huge amounts of text from the internet. They’re good at predicting the next word in a sentence, but because the internet is messy, they also learn its flaws. That means these systems can sometimes reproduce toxic speech, repeat harmful stereotypes or echo the loudest extremes of online debate.
“Current LLM-based tools tend to be similar in terms of their behavior and responses,” Davulcu says. “It’s not like a winner is emerging. They will need an edge. That edge is going to come from interpretability. How can we understand how these models make their decisions? And when they make incorrect decisions, how do we fix them?”
That’s where his new suite of tools comes in. Davulcu has filed four invention disclosures with ASU’s Skysong Innovations, outlining a method to make AI transparent, programmable and ultimately safer.
Opening the black box
The first innovation is a way to peek inside the “black box” of AI. Normally, these systems spit out answers without showing their reasoning. If they get something wrong, whether it’s suggesting glue on a pizza or giving bad medical advice, developers can’t easily correct the mistake.
Davulcu’s method changes that. His system translates the AI’s hidden decision-making into simple, editable rules. People can then adjust those rules, add exceptions, and feed the corrections back into the model.
“In order to build safe AI, you have to go beyond the black box,” he says. “You need to be able to see the rules the model is using, add exceptions and retrain it so that it doesn’t keep making the same mistake.”
Think of it as a feedback loop like wash, rinse and repeat. Reveal the logic, refine it, retrain and repeat. The goal, Davulcu explains, is an AI that won’t make a mistake. But if it does, the user will have a recourse to fix it instantly.
Making values visible
The second tool focuses on conversations. Most AI can tell if a comment sounds happy or angry, but that’s not enough for real-world debates. What matters is the stance: whether someone is for, against or neutral on an issue and why.
Davulcu’s team has built methods that can detect those stances and map how people cluster around them online. This makes it possible to see echo chambers, identify bridge-builders and highlight the shared values, such as fairness, safety or family, that people rally around.
“When you scale this, we can actually find the mean and the extremes,” he says. “And basically, at that point, we have a way of staying on the mean, avoiding the extremes, therefore getting rid of bias.”
Teaching AI to choose the mean
Once the system knows where the extremes are, it can be trained to avoid them. Davulcu’s group showed that if you feed AI examples of more balanced language and filter out toxic or divisive phrasing, the model learns to follow that path.
The results are promising. In tests, their approach reduced toxic output by 85% compared with an unrestricted model. Instead of echoing the most polarizing voices, the AI leans toward civil, respectful conversation.
“We’re showing that it’s possible to build systems that encourage civil discourse instead of amplifying division,” Davulcu says.
Maintaining transparency, no matter the model
Companies may want to swap AI models when a new version comes out, but those changes can break carefully tuned behavior. Davulcu’s fourth innovation is a control layer that travels with the application itself.
“What we want is for an application to keep working no matter which model you plug in underneath,” he says. “You should be able to switch from one to another because it’s cheaper or better, and your rules still apply. Everything still works. And if something goes wrong, you have a recourse for fixing it.”
From “smart” to socially responsible
Most attempts to make AI safe today rely on after-the-fact guardrails like extra filters or human reviewers bolted onto the system. Davulcu’s approach is different. It builds transparency, values and human correction mechanisms directly into the heart of the technology.
Aristotle taught us that virtue is the mean between two vices. More than two thousand years later, Davulcu is betting that the same wisdom can guide our machines as well as ourselves. By grounding AI in balance and shared human values, his work suggests a future where technology reasons in ways we can understand, trust and perhaps even call virtuous.
