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There’s an accurate way to confirm fraud: look for inconsistencies and replicate experiments. If the fraudsters “fail to replicate” legitimate experiments, ask

There’s an accurate way to confirm fraud: look for inconsistencies and replicate experiments. If the fraudsters “fail to replicate” legitimate experiments, ask them for details/proof, and replicate the experiment yourself while providing more details/proof. Either they’re running a different experiment, their details have inconsistencies, or they have unreasonable omissions.

wswope 8mo ago
Yeah, but this happens all the time. >>95% of the time, the fraudsters get off scot-free. Look at Dan Ariely: Caught red-handed faking data in Excel using the stupidest approach imaginable, and outed as a sex pest in the Epstein files. Duke is still giving him their full backing. It’s easy to find fraud, but what’s the point if our institutions have rotten all the way through and don’t care, even when there’s a smoking gun?
pixl97 8mo ago
Of course this is slightly messy too. Fraudsters are probably always incorrect, of course they could have stolen the data. But being incorrect doesn't mean your intentionally committing fraud.
john_strinlai 8mo ago
that approach is accurate, but not scalable. the effort to publish a fraudulent study is less (sometimes much less) than the effort to replicate a study.
awesome_dude 8mo ago
Is it that easy? Machine Learning papers, for example, used to have a terrible reputation for being inconsistent and impossible to replicate. That didn't make them (all) fraudulent, because that requires intent to deceive.
- itintheory 8mo ago
  What do you think it is about machine learning that makes it hard to replicate? I'm an outsider to academic research, but it seems like computer based science would be uniquely easy - publish the code, publish the data, and let other people run it. Unless it's a matter of scale, or access to specific hardware.
  - renewiltord 8mo ago
    A lot of things are easy if you ignore the incentive structure. E.g. a lot of papers will no longer be published if the data must be published. You’d lose all published research from ML labs. Many people like you would say “that’s perfectly okay; we don’t need them” but others prefer to be able to see papers like Language Models Are Few-Shot Learners https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165 So the answer is that we still want to see a lot of the papers we currently see because knowing the technique helps a lot. So it’s fine to lose replicability here for us. I’d rather have that paper than replicability through dataset openness.
    - armchairhacker 8mo ago
      But the lab must publish at least the general category of data, and if that doesn't replicate, then the model only works on a more specific category than they claim (e.g. only their dataset).
      - awesome_dude 8mo ago
        Even with the exact same dataset and architecture, ML results aren't perfectly replicable due to random weight initialisation, training data order, and non-deterministic GPU operations. I've trained identical networks on identical data and gotten different final weights and performance metrics. This doesn't mean the model only works on that specific dataset - it means ML training is inherently stochastic. The question isn't 'can you get identical results' but 'can you get comparable performance on similar data distributions.
        8mo ago
ertgbnm 8mo ago
That would be great if journals bothered publishing replication studies. But since they don't, researchers can't get adequate funding to perform them, and since they can't perform them, they don't exist. We can't look for failed replication experiments if none exist.
mike_hearn 8mo ago
That only confirms a very small subset of fraud. There are many ways to do scientific fraud that will yield internally consistent papers that pass replication as practiced today. An example is papers which claims of the form, "We proved X by doing Y" where Y is a methodology that isn't derived from and can't prove X. This sort of paper will replicate every time because if you re-derive a correct methodology the original authors say you didn't really replicate their study and your work should be ignored, but if you use their broken methodology you'll just give an intellectually fraudulent paper the stamp of replication approval. This kind of problem is actually much more widespread than work that looks scientific but in which the data is faked.

wswope 8mo ago

Yeah, but this happens all the time. >>95% of the time, the fraudsters get off scot-free. Look at Dan Ariely: Caught red-handed faking data in Excel using the stupidest approach imaginable, and outed as a sex pest in the Epstein files. Duke is still giving him their full backing. It’s easy to find fraud, but what’s the point if our institutions have rotten all the way through and don’t care, even when there’s a smoking gun?

pixl97 8mo ago

Of course this is slightly messy too. Fraudsters are probably always incorrect, of course they could have stolen the data. But being incorrect doesn't mean your intentionally committing fraud.

john_strinlai 8mo ago

that approach is accurate, but not scalable. the effort to publish a fraudulent study is less (sometimes much less) than the effort to replicate a study.

awesome_dude 8mo ago

Is it that easy? Machine Learning papers, for example, used to have a terrible reputation for being inconsistent and impossible to replicate. That didn't make them (all) fraudulent, because that requires intent to deceive.

itintheory 8mo ago
What do you think it is about machine learning that makes it hard to replicate? I'm an outsider to academic research, but it seems like computer based science would be uniquely easy - publish the code, publish the data, and let other people run it. Unless it's a matter of scale, or access to specific hardware.
- renewiltord 8mo ago
  A lot of things are easy if you ignore the incentive structure. E.g. a lot of papers will no longer be published if the data must be published. You’d lose all published research from ML labs. Many people like you would say “that’s perfectly okay; we don’t need them” but others prefer to be able to see papers like Language Models Are Few-Shot Learners https://arxiv.org/abs/2005.14165 https://arxiv.org/abs/2005.14165 So the answer is that we still want to see a lot of the papers we currently see because knowing the technique helps a lot. So it’s fine to lose replicability here for us. I’d rather have that paper than replicability through dataset openness.
  - armchairhacker 8mo ago
    But the lab must publish at least the general category of data, and if that doesn't replicate, then the model only works on a more specific category than they claim (e.g. only their dataset).
    - awesome_dude 8mo ago
      Even with the exact same dataset and architecture, ML results aren't perfectly replicable due to random weight initialisation, training data order, and non-deterministic GPU operations. I've trained identical networks on identical data and gotten different final weights and performance metrics. This doesn't mean the model only works on that specific dataset - it means ML training is inherently stochastic. The question isn't 'can you get identical results' but 'can you get comparable performance on similar data distributions.
      - 8mo ago

ertgbnm 8mo ago

That would be great if journals bothered publishing replication studies. But since they don't, researchers can't get adequate funding to perform them, and since they can't perform them, they don't exist. We can't look for failed replication experiments if none exist.

mike_hearn 8mo ago

That only confirms a very small subset of fraud. There are many ways to do scientific fraud that will yield internally consistent papers that pass replication as practiced today. An example is papers which claims of the form, "We proved X by doing Y" where Y is a methodology that isn't derived from and can't prove X. This sort of paper will replicate every time because if you re-derive a correct methodology the original authors say you didn't really replicate their study and your work should be ignored, but if you use their broken methodology you'll just give an intellectually fraudulent paper the stamp of replication approval. This kind of problem is actually much more widespread than work that looks scientific but in which the data is faked.