Katarina Slama
Katarina Slama is an AI researcher whose work connects human attention research, language-model alignment and the societal impacts of AI. Her path ran from small-town Finland to US universities, where she earned a UC Berkeley neuroscience PhD focused on human attention; in 2021, she led a study of visual search using intracranial recordings. At OpenAI, she contributed to InstructGPT, published in 2022, and coauthored OpenAI's GPT-4 Technical Report in 2023. Her later research compared AI-scheming research with 1970s studies of ape language and tested how language-model preferences shape donation advice and refusals. As of October 2026, she is an AI Societal Impacts Research Scientist at the UK AI Security Institute; in September 2026, she signed a statement calling for limits on recursive self-improvement until it can be done safely.
- Now at
- UK AI Security Institute
- Badges
Timeline
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2021
Visual search at Berkeley
Her path runs from small-town Finland to US universities, and she pivots from neuroscience research to AI. In 2021, she leads a visual-search study in Robert Knight's lab at UC Berkeley's Helen Wills Neuroscience Institute, using intracranial recordings from patients awaiting surgery for epilepsy.
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2022
InstructGPT with human feedback
At OpenAI, she contributes to the development of InstructGPT, published in 2022. The paper fine-tunes GPT-3 with labeler demonstrations and rankings of model outputs, then uses reinforcement learning from human feedback.
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March 2023
The GPT-4 technical report
In March 2023, she coauthors OpenAI's GPT-4 Technical Report, which describes GPT-4 as a multimodal model that accepts image and text inputs and produces text outputs.
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July 2025
Comparing AI scheming research
In July 2025, she coauthors a paper comparing research on AI scheming with 1970s tests of whether non-human primates could master natural language. It argues that research into AI scheming should avoid overattributing human traits to other agents.
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August 2026
Testing LLM preferences
In August 2026, she coauthors a TMLR paper on whether language-model preferences predict downstream behavior. Across five frontier LLMs, the study finds that all five give preference-aligned donation advice and refuse more often for less-preferred entities.
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September 2026
Limits on recursive self-improvement
On 24 September 2026, she signs a civil-society statement urging governments to limit recursive self-improvement until it can be done safely.
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As of October 2026
AI societal impacts at UK AISI
In December 2025, she researches societal impacts of transformative AI with the UK AI Security Institute and holds a UC Berkeley neuroscience PhD focused on mechanisms of human attention allocation. As of October 2026, she is an AI Societal Impacts Research Scientist at the institute.
Career
- Since 2025UK AI Security InstituteAI Societal Impacts Research Scientist
- 2022OpenAI
- 2021University of California, Berkeley
Education
- University of California, BerkeleyPhD in Neuroscience
Sources and credits
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- Podcast about artificial intelligence: Ja ihminen loi älyn | Aalto University
- Looking for something? Recording directly from the human brain reveals mechanisms underlying visual search | Helen Wills Neuroscience Institute
- Training language models to follow instructions with human feedback
- Introduction to AI Safety Institutes with Tutke and researchers from UK AISI
- GPT-4 Technical Report
- Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language
- Lessons from a chimp: AI "scheming" and the quest for ape language
- Transactions on Machine Learning Research
- When Do LLM Preferences Predict Downstream Behavior?
- Statement on RSI 2026 — AI Safety Connect


