%0 Conference Paper
%A Rathkopf, Charles
%T Deep learning models in science: some risks and opportunities
%M FZJ-2024-05893
%D 2024
%X Deep neural networks offer striking improvements in predictive accuracy in many areas of science, and in biological sequence modeling in particular. But that predictive power comes at a steep price: we must give up on interpretability. In this talk, I argue - contrary to many voices in AI ethics calling for more interpretable models - that this is a price we should be willing to pay.
%B Helmholtz workshop on the ethics of AI in scientific practice
%C 11 Jun 2024, Jülich/Düsseldorf (Germany)
Y2 11 Jun 2024
M2 Jülich/Düsseldorf, Germany
%F PUB:(DE-HGF)31
%9 Talk (non-conference)
%U https://juser.fz-juelich.de/record/1031969