Propensity Inference: Environmental Contributors to Unsanctioned LLM Behaviour

olli järviniemi, Oliver Makins, Jacob Merizian, Robert Kirk, Ben Millwood

Abstract:

Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring
language models’ propensity for unsanctioned behaviour. We contribute three methodological improvements:
analysing effects of changes to environmental factors on behaviour, quantifying effect sizes via Bayesian generalised linear models, and taking explicit measures against circular analysis. We apply the methodology to
measure the effects of 12 environmental factors (6 strategic in nature, 6 non-strategic) and thus the extent to
which behaviour is explained by strategic aspects of the environment, a question relevant to risks from misalignment. Across 23 language models and 11 evaluation environments, we find approximately equal contributions
from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becoming more
or less influential as capabilities improve, and find some evidence for a trend for increased sensitivity to goal
conflicts. Finally, we highlight a key direction for future propensity research: the development of theoretical
frameworks and cognitive models of AI decision-making into empirically testable forms.