Proprietary models make it hard to spot faulty algorithms, Greene says, and greater transparency could help to prevent them from becoming so widely deployed. “At the end of the day,” Greene says, “we have to ask, ‘Are we deploying a bunch of algorithms in practice that we can’t understand, for which we don’t know their biases, and that might create real harm for people?’ ”
Making models and data publicly available helps everyone, says Emma Lundberg, a bioengineer at Stanford University in California, who has applied machine learning to protein imaging. “Then someone could use it on their own data set and find, ‘Oh, it’s not working perfectly, so we’re going to tweak it’, and then that tweak is going to make it applicable elsewhere,” she says.
the creation of standards for collecting data and reporting machine-learning studies, allowing participants to give consent to the use of their data, and adopting approaches that ensure rigorous and privacy-preserving analyses. For example, an effort called the Observational Medical Outcomes Partnership Common Data Model allows patient and treatment information to be collected in the same way across institutions.
Eliminating data redundancy would also help, says Søren Brunak, a translational-disease systems biologist at the University of Copenhagen. In machine-learning studies that predict protein structures, he says, scientists have had success in removing proteins from test sets that are too similar to proteins used in training sets.
Multiple machine-learning checklists are already available, many spearheaded by the Equator Network, an international initiative to improve the reliability of health research. The
self-supervised learning.
AI is what is fed to the AI but artificial IN NOT intelligent 🤬
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