I've been thinking about how with the rise of LLMs, we're going to uncover A LOT of "bad studies" over the next decade. Could be some sort of mass reckoning. Probably better to admit it all now than be uncovered in 5 years.
Presumably the same can be done for tabular data and genomic or "omics" data. At the very least statistical techniques can be used. I imagine high throughput imaging modalities will be the main target.
That being said the only way the -omics data will have utility is via AI models which are trained on some task... which present their own problems..
You don't need a language model. The effort is in collecting and chopping the data to find snippets to compare. NNs can help with that.
Newer fraud will probably use generative diffusion AIs to make "realistic western plots" on demand .. there's probably a paper in that!
Agreed that de-anonymizing will become trivial. This will be a problem not only for bad actors in research, journalism, creative writing, etc., but for internet commenters who believed they'd done due diligence to remain anonymous, and even for research participants who'd expected anonymity when signing their consent forms. We're rushing headlong into even stranger days!
Academic fraud seems to be self-correcting as we have seen by the numerous reports of fraud and withdrawn papers; as a rule, the players express shame and remorse. Nonacademic fraud doesn't exhibit these characteristics and sometimes seems to be proud of the fraud.