A useful sounding of the alarm about the expertise crisis, but:

"we advocate for a standard where bugs and other errors in data analysis that change or challenge a paper's findings constitute irreproducibility."

Please no. One of the biggest problems I face when talking to people about bad science in any context is the belief that "peer reviewed and reproducible" is a synonym for correct. The phrase the authors are looking for here is not irreproducible, but rather something like: flawed, incorrect, biased, pseudo-scientific, or even intellectually fraudulent. The danger of trying to redefine the word reproducible to mean more than "do people get the same results" is threefold:

1. Researchers will reject claims their work is not reproducible by saying "actually they didn't do the same things we did so of course they didn't get the same results" and that will be a convincing rebuttal to outsiders who don't dig into the details.

2. It would further undermine trust in academic research. Way too frequently, I read a paper that makes an interesting claim, only to discover that their paper or maybe entire field has redefined common words in ways that make the claims misleading.

3. It doubles down on the unhelpful and probably counter-productive "reproducibility crisis" framing.

Why unhelpful, well, there isn't really a reproducibility crisis. What we have here is actually an intellectual fraud crisis. After reading a ton of papers from outside CS in past few years it became impossible to avoid the uncomfortable conclusion that in many fields the majority of observable errors are not really errors at all, but are actually deliberate. Or at least, they are deliberately fooling themselves which amounts to the same thing.

To highlight just a few examples of mistakes where you think, how can nobody have noticed this:

• (from the linked paper) "a recent study included the use of anti-hypertensive drugs as a feature for predicting hypertension. Such a feature could lead to leakage because the model would not have access to this information when
predicting the health outcome for a new patient. Further, if the fact that a patient uses anti-hypertensive drugs is already
known at prediction time, the prediction of hypertension becomes a trivial task"

• A widely used ML model from social science that claimed to predict if a Twitter account is a bot was put online and found to have an FP rate of 50%+. When this was pointed out by third parties, the response was to claim the testers were "academic trolls". Nothing was ever retracted and the model continued to be used in new papers across the field.

• A COVID modelling paper blithely computed that the average Brit lives with 7 people. This was obviously wrong both in absolute values and just being nonsensical design to begin with (that should be an input taken from census data not an output), and the peer reviewer even noticed this but approved the paper anyway.

• The Ferguson Report 9 model that directly led to lockdowns in the UK and other countries, was full of computational bugs like typos in their custom PRNG, out of bounds memory reads, bugs in a custom Fisher-Yates shuffle, thread safety errors and more. Nobody in the academic world appeared to care about this.

The paper authors suggest making researchers fill out more paperwork to get published. I find it impossible to believe that this will work. Mandatory signed data sharing statements didn't work: there was a study posted on HN in the past few months in which someone tested this to see if the data was genuinely made available on request and something like >90% of scientists refused (in epidemiology I think). Similar results were found in other fields. In this light the mass adoption of ever more opaque and buggy statistical/computational techniques is not merely an accidental drift, correctable with a minor bit of bureaucratic oversight. These techniques seem to be popular exactly because they grant so many angles of freedom to get away with scientific murder.