Compare AI and human neuroimaging data on animacy and biological vulnerability, integrate brain sensory representations into AI, and publish open-source biological validation guidelines.
Compare AI and human neuroimaging data on animacy and biological vulnerability, integrate brain sensory representations into AI, and publish open-source biological validation guidelines.
Project Details
Updated 07/13/26 · Edited by orgDuring the MPhil studies, I will conduct research with a comparative analysis of current AI systems of animacy, physiological and biological vulnerability of living beings and compare it to neurobiological human data. Using human visuo-semantic neuroimaging datasets, I will integrate the sensory representations of the human brain into AI systems and test their alignment. I am to produce neuroscientifically based learning algorithms that can perceive the fragility of biological organisms and the precipice of animacy (livingness) of different entities: humans, animals, insects, and other organisms.
The research will be carried out at Prof. Kamila Jozwik’s lab at the University of Cambridge. Prof. Jozwik specializes in biologically-inspired deep learning and AI models. My previous research includes studies of human-machine interactions and the usage of AI chatbots in mental health. In this study, I saw that AI is not ready yet to fully understand the emotional spectrum and sometimes even physiological boundaries of humans. It inspired me to conduct the proposed research on AI’s concept of animacy and contribute research findings on how neuroscience can help us build more empathetic and intelligent AI systems.
The research will be conducted within 12 months, where the first 9 months will be for full-time work on the research. The last 3 months will be for writing AI design frameworks for tech companies based on the results of the experience and sharing them with the AI safety community through the Leverhulme Centre for the Future of Intelligence in Cambridge.
The end goal of the project is to develop guidelines for biological validation of AI models and publish them open-source.
Theory of Impact
Updated 10/07/26 · By grantmaking.aiThe goal of my study will be to investigate how AI's internal representations are misaligned from human ones, which could lead to higher risks of physical harm to living things by AI in the future. Currently, many companies are integrating AI in various fields, including medicine, mental health, and psychological counselling. However, AI still can’t completely understand the fragility of human life, which can lead to injuries and even death.
With neurobiological validation of these concepts, we will be able to teach AI to be more perceptive of the vulnerability of living organisms and improve the safety of neural models in risky fields.
People
Updated 10/07/26 · By grantmaking.aiTeam Member
Hello Elena,
You you rise a topic I find quite upstream. It is true, before any harm prevention regulation comes into play, there is the need for the system to recognize that there is something fragile in front of it which can actually be harmed. As far as I understand, you test the correctness of such a recognition through human brain imaging data, and therefore it becomes verifiable.
This problem was assigned to philosophy from the beginning, I guess.
I think you found an innovative solution to an extremely important and challenging risk.
One question came to my mind: what makes the right marking of user's fragility influence the behavior of the model? Recognition is different from caring, and I wonder where the point of contact between them is. Perhaps you see it yourself already.
I support your project.