As AI becomes more embedded in daily life, concerns are growing over how algorithms influence human behavior — from reinforcing biases to fueling polarization. But Lee Dae-yeol, a South Korean neuroscientist and professor at Johns Hopkins University School of Medicine, believes the key to developing AI that serves society lies in understanding one of the brain’s most mysterious traits: altruism.

Social mammals often help others at their own expense,” Lee said in a recent video interview with Chosun Ilbo. “If we can figure out how altruism works in the brain, we could design AI systems that reflect those values.”

Below are highlights from the interview, lightly edited for clarity and brevity.

Lee Dae-yeol, a professor at Johns Hopkins University School of Medicine, opens his lab in Maryland to Chosun Ilbo for an interview on March 17, 2025. Lee is currently studying how neurons function in monkey brains, though the research subjects could not be photographed for the story./Lee Dae-yeol’s lab

How does neuroscience explain altruism?

“Though imperfect, the human brain is capable of balancing self-interest and social behavior. Altruism is one of the most difficult behaviors to study scientifically. Neuroscientists have been exploring it for about 20 years. Recent studies show that when animals help others in distress, neurons in the hypothalamus and amygdala are activated. Interestingly, when humans take revenge despite a personal cost, regions like the striatum also light up — the same area involved when people donate to charity. It seems tied to a sense of satisfaction.”

Can these brain functions be applied to AI?

“Early AI systems were modeled after the human brain — artificial neural networks. If we can better understand how the brain supports altruistic behavior, we could improve AI algorithms and even use them to tackle social problems. A deeper grasp of the brain will be essential to developing more advanced AI.”

What would that look like in practice?

“We’re still at the beginning, but one idea is to simulate how brain regions involved in altruistic decision-making interact, and then train AI to replicate that architecture. We might also design systems that, once trained, can’t override those patterns. In essence, we’d be hardcoding altruism.”

Would that bring us closer to artificial general intelligence?

“Not exactly. True intelligence requires desire — the kind living beings have. Intelligence evolved from basic survival and reproductive instincts. AI doesn’t have needs or desires; it doesn’t think for itself, it just processes data. At best, it’s an extremely sophisticated machine.”

Could reliance on AI hinder human intelligence?

“The brain never stops evolving — at least biologically. What changes is its direction, depending on the environment. With AI, the human brain may evolve to collaborate more closely with machines. That doesn’t necessarily mean a decline in intelligence. But we should be cautious. Social media algorithms, for instance, reinforce confirmation bias and can push people toward more extreme views.”

What are you currently researching?

“We’re studying how monkeys play a version of Baduk (Go) with a computer, while tracking how their brain cells respond. Some neurons activate when the monkeys are winning and quiet down when they’re losing — suggesting a role in outcome assessment. This could shed light on the brain’s decision-making process. Many mental disorders, like dementia or autism, involve disrupted decision-making. Understanding how the brain works helps us identify what’s malfunctioning — and may help AI better mimic human reasoning.”

Neuralink, founded by Elon Musk, announced in 2021 that it enabled monkeys to play a computer game using only its brain signals./Neuralink

What do you think of ideas like cloning the human brain, as seen in movies like Mickey 17?

“That’s not realistic. Even if you replicate 99.9% of the brain, that last 0.1% makes all the difference. A fully cloned human brain is fantasy. That said, Elon Musk’s Neuralink idea — implanting chips to enable closer brain-machine interaction — has some merit. It’s about controlling neural activity, which is a promising field of research.”

Lee helped pioneer the field of neuroeconomics, which merges neuroscience with decision theory to study how neurons behave during decision-making. He originally majored in economics at Seoul National University but shifted to brain science after becoming fascinated with the biological roots of thought and emotion.

Why switch from economics to neuroscience?

“I initially wanted to be a physicist. But while studying economics, I became interested in psychology-related fields like game theory. Learning that all thought and emotion come from brain activity made me want to study biology. I took about eight biology courses before graduating, then went abroad for graduate studies with support from the Korea Foundation for Advanced Studies.”

Was the transition difficult?

“It was tough. Writing weekly lab reports in English, when I’d never written one before, was painful. But that awareness of my lack of background made me work even harder. That mindset helped when I started researching complex decision-making in monkeys. There weren’t many others in the field, so I had to read widely across disciplines. I’ve now been in this field for 25 years. The early struggles became strengths.”

How does Korea’s brain science field compare to the U.S.?

“Across the board, Korea’s biggest hurdle is infrastructure. There just aren’t enough resources. At our university in the U.S., more researchers study the brain using monkeys than in all of Korea. It’s hard to sustain and grow a field under those conditions.”

Lee Dae-yeol

After earning a degree in economics from Seoul National University in 1989, Lee received a Ph.D. in neuroscience from the University of Illinois. A pioneer in neuroeconomics, he studies how the brain governs decision-making, particularly in irrational contexts. His work contributes to understanding psychiatric conditions such as depression, addiction, and OCD. He taught at Yale University before joining Johns Hopkins University School of Medicine in 2019.