AI to Accelerate Scientific Understanding Workshop

Machine learning models have demonstrated a vast capacity to learn complex phenomena from data and to reproduce them accurately, for example by providing precise predictions of molecular properties or by generating human language in natural conversations. However, due to their black-box nature, such models often resemble oracles with vast stores of knowledge rather than scientific tools capable of reporting or communicating the reasoning underlying their predictions. This limitation constrains their potential impact in scientific applications, where understanding why a model arrives at a prediction can be as important as the prediction itself and can substantially accelerate scientific discovery and understanding.

In this workshop, we seek to develop a broader and more systematic understanding of how the explanation and interpretation of artificial intelligence models can enable AI-driven research from both theoretical and applied perspectives. In particular, we aim to explore how AI-driven research can be enriched beyond model validation and trust assessment.

The workshop will include invited talks, poster sessions, and dedicated time for discussion, fostering informal exchange across disciplines such as molecular science, medicine, digital humanities, geoscience, and related fields.

The event is co-sponsored by ELIZA - the Konrad Zuse School of Excellence in Learning and Intelligent Systems.