As someone who did a PhD and published and read many papers (in ML) I believe vast majority of papers in ML are misleading. If in fact every paper that claims a better performance over state of the art was true we would have solved AI by now. You see all sorts of problems when you dig deeper into the technical details of peer reviewed publications (even in top tier conferences) including misleading baselines, statistical insignificance of improvements, overfitting to test data, and in some cases just flat-out fabrication of results.
I hope this is not as bad in medicine and health related research. But just thinking that some paper can be used against you in court to claim billions of dollars in damages makes me uneasy. Peer reviewed paper != science. Peer review is a crude filter on research that can both accept bad work and reject good work. There must be a higher bar for something that can be used in the court of law. Some sort of scientific consensus must be needed at least.
It's easy to dismiss this because screw J&J. But I think we are all paying for these lawsuits through our insurances and taxes and higher drug prices.
Not saying these lawsuits don't have merits, but I think there must be a higher bar for what is presented as evidence.
I hope this is not as bad in medicine and health related research. But just thinking that some paper can be used against you in court to claim billions of dollars in damages makes me uneasy. Peer reviewed paper != science. Peer review is a crude filter on research that can both accept bad work and reject good work. There must be a higher bar for something that can be used in the court of law. Some sort of scientific consensus must be needed at least.
It's easy to dismiss this because screw J&J. But I think we are all paying for these lawsuits through our insurances and taxes and higher drug prices.
Not saying these lawsuits don't have merits, but I think there must be a higher bar for what is presented as evidence.