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@INPROCEEDINGS{Rathkopf:1031969,
      author       = {Rathkopf, Charles},
      title        = {{D}eep learning models in science: some risks and
                      opportunities},
      reportid     = {FZJ-2024-05893},
      year         = {2024},
      abstract     = {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.},
      month         = {Jun},
      date          = {2024-06-11},
      organization  = {Helmholtz workshop on the ethics of AI
                       in scientific practice,
                       Jülich/Düsseldorf (Germany), 11 Jun
                       2024},
      subtyp        = {Other},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5255 - Neuroethics and Ethics of Information (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5255},
      typ          = {PUB:(DE-HGF)31},
      url          = {https://juser.fz-juelich.de/record/1031969},
}