From Transparency to Reliability: Using AI Responsibly in Science

Abstract

Artificial intelligence increasingly automates key steps in scientific work, from data analysis to hypothesis generation. Because many deployed models lack transparency, far-reaching decisions about time, resources, and funding often rest on algorithmic processes whose derivation cannot be directly traced. In publicly funded science, this raises fundamental questions of responsibility.

The lecture argues that responsible AI use is not primarily about transparency. Rather, what matters is the demonstrable reliability of scientific workflows—whether an AI-assisted research process leads reliably to accurate results. Since the quality of complex neural networks cannot be assessed by examining their internal mechanisms, this reliability must be secured at the workflow level, through theoretically grounded training data, robust validation studies, and systematic error analyses.

Using protein structure prediction and neuroimaging-based psychiatric prognostication as contrasting examples, the lecture shows that the conditions for such assurance vary significantly across disciplines, making responsible practice a context-sensitive question of science ethics rather than a purely technical matter.

Charles Rathkopf

I am interested in how mental properties emerge from physical stuff.