4 ms·

More than half of researchers now use AI for peer review, often against guidance

63 points by neilv 10mo ago

D-Machine 10mo ago
Duplicate: https://news.ycombinator.com/item?id=46281961 https://news.ycombinator.com/item?id=46281961
- croes 10mo ago
  6 points no comments vs 18 points and 2 comments. Faster isn’t the metric here
  - D-Machine 10mo ago
    Am I missing something here? I am new to posting at HN, despite being a long-time reader. I get that HN has a policy to allow duplicates so that duplicates that were missed for arbitrary timing reasons can still gain traction at later times. I've seen plenty of "[Duplicate]" tagged posts, and have just seen this as a sort of useful thing for readers (duplicates may have interesting info, or seeing that the dupe did or did not gain traction also gives me info). But maybe I am missing something here, particularly etiquette-wise?
    - kachapopopow 10mo ago
      better title is most often the reason for it, looking at it the em-dash probably caused people to dismiss it as an AI bot.
      - D-Machine 10mo ago
        If that's the simplistic heuristic people here are using...
        kachapopopow 10mo ago
        if you turn on "show dead" you will see a lot of spam / AI comments
        D-Machine 10mo ago
        Yeah I have that on so see those all the time, I was more wondering why I got a strange comment about tagging a duplicate, and was wondering if I was breaching some kind of etiquette.
        kachapopopow 10mo ago
        people with 100k+ karma often breach the etiquitte they preach so I wouldn't worry too much about it, worse case you get downvoted to -5 and it'll become dead.
        D-Machine 10mo ago
        Ok, figured basically that, but very much appreciate the confirmation. Thanks!
    - layer8 10mo ago
      It’s certainly okay to link to a previous discussion, but “duplicate” implies that you think the present submission shouldn’t exist, and the previous submission doesn’t actually provide any discussion. The fact that a previous submission didn’t gain traction isn’t usually interesting, because it can be pretty random whether something gains traction or not, depending on time of day and audience that happens to be online.
      - D-Machine 10mo ago
        Okay, I don't in general see "duplicate" as implying this, but I take your point, and was wondering if that might be the etiquette here. I also think, on reflection, that you are right in this particular case (given there are no comments on the previous duplicate) so, thank you also for clarifying. I suppose in the future an e.g. "[Previous discussion]" tag would be more appropriate, providing comments were made, otherwise, just say nothing and leave it to HN.
N_Lens 10mo ago
News: Half of researchers lied on this survey
- vinni2 10mo ago
  Which half?
baalimago 10mo ago
They should do a study on this.
bpodgursky 10mo ago
Journals need to find a way to give guidance on what is and isn't appropriate and to let reviewers explain how they used AI tools... because like, you aren't going to nag people out of using AI to do UNPAID work 90% faster and produce results that are 90+th percentile of review quality (let's be real, there are a lot of bad flesh and blood reviewers).
kachapopopow 10mo ago
I think it's interesting that AI is probably unintuitively good at spotting fraud in papers due to their ability to hold more context than majority of humans. I wish someone explored this to see if it can spot academic fraud that isn't in their training data already.
- BDPW 10mo ago
  LLM's still make stuff up routinely about things like this so no there's no way this is a reliable method.
  - kachapopopow 10mo ago
    It doesn't have to be reliable! It just has to flag things: "hey these graphs look like they were generated using (formula)" or "these graphs do not seem to represent realistic values / real world entrophy" - it just has to be a tool that stops very advanced fraud from slipping through when it already passed human peer review. The only reason why this is helpful is because humans have natural biases and/or inverse of AI biases which allow them to find patterns that might just be the same graph being scaled up 5 to 10 times.
    - BDPW 10mo ago
      I hope I'm wrong but I haven't seen anything like this in practice. I would imagine we have the same problem as before where we could use it as an extra filter but the amount of shit that comes out makes the process not actually any more accurate, just faster. Having seen from close-up how these reviews go, I get why people use tools like this unfortunately. it doesn't make me very hopeful for the near future of reviewing.
      - kachapopopow 10mo ago
        that's why I wanted someone to actually do a real test to see if it has even a shred of accuracy since that means it could be improved upon in the future.
  - 10mo ago
D-Machine 10mo ago
Guidance needs to be more specific. Failing to use AI for search often means you are wasting a huge amount of time, ChatGPT 5.2 Extended Thinking with search enabled speeds up research obscenely, and I'd be more concerned if reviewers were NOT making use of such tools in reviews. Seeing the high percentage of usage of AI for composing reviews is concerning, but, also, peer review is an unpaid racket which seems basically random anyway (https://academia.stackexchange.com/q/115231 https://academia.stackexchange.com/q/115231), and probably needs to die given alternatives like ArXiV and OpenPeerReview and etc. I'm not sure how much I care about AI slop contaminating an area that already might be mostly human slop in the first place.
- jltsiren 10mo ago
  That's a wrong way of using AI in peer review. A key part of reviewing a paper is reading it without preconceptions. After you have done the initial pass, AI can be useful for a second opinion, or for finding something you may have missed. But of course, you are often not allowed to do that. Review copies are confidential documents, and you are not allowed to upload them to random third-party services. Peer review has random elements, but thats true for all other situations (such as job interviews), where the final decision is made using subjective judgment. There is nothing wrong in that.
  - D-Machine 10mo ago
    > A key part of reviewing a paper is reading it without preconceptions I get where you are coming from here, but, in my opinion, no, this is not part of peer review (where expertise implies preconceptions), nor for really anything humans do. If you ignore your pre-conceptions and/or priors (which are formed from your accumulated knowledge and experience), you aren't thinking. A good example in peer review (which I have done) would be: I see a paper where I have some expertise of the technical / statistical methods used in a paper, but not of the very particular subject domain. I can use AI search to help me find papers in the subject domain faster than I can on my own, and then I can more quickly see if my usual preconceptions about the statistical methods are relevant on this paper I have to review. I still have to check things, but, previously, this took a lot more time and clever crafting of search queries. Failing to use AI for search in this way harms peer review, because, in practice, you do less searching and checking than AI does (since you simply don't have the time, peer review being essentially free slave labor).
zeofig 10mo ago
This is because peer review has become a bullshit mill and AI is good at churning through/out bullshit.
TomasBM 10mo ago
The reasons listed in TFA - "confidentiality, sensitive data and compromising authors’ intellectual property" - make sense to discourage reviewers from using cloud-based LLMs. There are also reasons for discouraging the use LLMs in peer review at all: it defeats the purpose of peer in the peer review; hallucinations; criticism not relevant to the community; and so on. However, I think it's high time to reconsider what scientific review is supposed to be. Is it really important to have so-called peers as gatekeepers? Are there automated checks we can introduce to verify claims or ensure quality (like CI/CD for scientific articles), and leave content interpretation to the humans? Let's make the benefits and costs explicit: what would we be gaining or losing if we just switched to LLM-based review, and left the interpretation of content to the community? The journal and conference organizers certainly have the data to do that study; and if not, tool providers like EasyChair do.
- deleted 10mo ago
  [deleted]
- pastage 10mo ago
  When you measure something people will start to game it. CI/CD needs a firm hand to work.
- conartist6 10mo ago
  Better to completely drop review, don't you think? LLMs simply don't do science. They have no integrity. There is no "bullshit me" step in the scientific process
- i_am_proteus 10mo ago
  Yes, there are often strong reasons to have peers as gatekeepers. Scientific writing is extremely information-dense. Consider a niche technical task that you work on -- now consider summarizing a day's worth of work in one or two sentences, designed to be read by someone else with similar expertise. In most scientific fields, the niches are pretty small, The context necessary to parse that dense scientific writing into a meaningful picture of the research methods is often years/decades of work in the field. Only peers are going to have that context. There are also strong reasons why the peers-as-gatekeepers model is detrimental to the pursuit of knowledge, such as researchers forming semi-closed communities that bestow local political power on senior people in the field, creating social barriers to entry or critique. This is especially pernicious given the financial incentives (competition for a limited pool of grant money; award of grant money based on publication output) that researchers are exposed to.

63 points by neilv 10mo ago

D-Machine 10mo ago

Duplicate: https://news.ycombinator.com/item?id=46281961 https://news.ycombinator.com/item?id=46281961

croes 10mo ago
6 points no comments vs 18 points and 2 comments. Faster isn’t the metric here
- D-Machine 10mo ago
  Am I missing something here? I am new to posting at HN, despite being a long-time reader. I get that HN has a policy to allow duplicates so that duplicates that were missed for arbitrary timing reasons can still gain traction at later times. I've seen plenty of "[Duplicate]" tagged posts, and have just seen this as a sort of useful thing for readers (duplicates may have interesting info, or seeing that the dupe did or did not gain traction also gives me info). But maybe I am missing something here, particularly etiquette-wise?
  - kachapopopow 10mo ago
    better title is most often the reason for it, looking at it the em-dash probably caused people to dismiss it as an AI bot.
    - D-Machine 10mo ago
      If that's the simplistic heuristic people here are using...
      - kachapopopow 10mo ago
        if you turn on "show dead" you will see a lot of spam / AI comments
        D-Machine 10mo ago
        Yeah I have that on so see those all the time, I was more wondering why I got a strange comment about tagging a duplicate, and was wondering if I was breaching some kind of etiquette.
        kachapopopow 10mo ago
        people with 100k+ karma often breach the etiquitte they preach so I wouldn't worry too much about it, worse case you get downvoted to -5 and it'll become dead.
        D-Machine 10mo ago
        Ok, figured basically that, but very much appreciate the confirmation. Thanks!
  - layer8 10mo ago
    It’s certainly okay to link to a previous discussion, but “duplicate” implies that you think the present submission shouldn’t exist, and the previous submission doesn’t actually provide any discussion. The fact that a previous submission didn’t gain traction isn’t usually interesting, because it can be pretty random whether something gains traction or not, depending on time of day and audience that happens to be online.
    - D-Machine 10mo ago
      Okay, I don't in general see "duplicate" as implying this, but I take your point, and was wondering if that might be the etiquette here. I also think, on reflection, that you are right in this particular case (given there are no comments on the previous duplicate) so, thank you also for clarifying. I suppose in the future an e.g. "[Previous discussion]" tag would be more appropriate, providing comments were made, otherwise, just say nothing and leave it to HN.

N_Lens 10mo ago

News: Half of researchers lied on this survey

vinni2 10mo ago
Which half?

baalimago 10mo ago

They should do a study on this.

bpodgursky 10mo ago

Journals need to find a way to give guidance on what is and isn't appropriate and to let reviewers explain how they used AI tools... because like, you aren't going to nag people out of using AI to do UNPAID work 90% faster and produce results that are 90+th percentile of review quality (let's be real, there are a lot of bad flesh and blood reviewers).

kachapopopow 10mo ago

I think it's interesting that AI is probably unintuitively good at spotting fraud in papers due to their ability to hold more context than majority of humans. I wish someone explored this to see if it can spot academic fraud that isn't in their training data already.

BDPW 10mo ago
LLM's still make stuff up routinely about things like this so no there's no way this is a reliable method.
- kachapopopow 10mo ago
  It doesn't have to be reliable! It just has to flag things: "hey these graphs look like they were generated using (formula)" or "these graphs do not seem to represent realistic values / real world entrophy" - it just has to be a tool that stops very advanced fraud from slipping through when it already passed human peer review. The only reason why this is helpful is because humans have natural biases and/or inverse of AI biases which allow them to find patterns that might just be the same graph being scaled up 5 to 10 times.
  - BDPW 10mo ago
    I hope I'm wrong but I haven't seen anything like this in practice. I would imagine we have the same problem as before where we could use it as an extra filter but the amount of shit that comes out makes the process not actually any more accurate, just faster. Having seen from close-up how these reviews go, I get why people use tools like this unfortunately. it doesn't make me very hopeful for the near future of reviewing.
    - kachapopopow 10mo ago
      that's why I wanted someone to actually do a real test to see if it has even a shred of accuracy since that means it could be improved upon in the future.
- 10mo ago

D-Machine 10mo ago

Guidance needs to be more specific. Failing to use AI for search often means you are wasting a huge amount of time, ChatGPT 5.2 Extended Thinking with search enabled speeds up research obscenely, and I'd be more concerned if reviewers were NOT making use of such tools in reviews. Seeing the high percentage of usage of AI for composing reviews is concerning, but, also, peer review is an unpaid racket which seems basically random anyway (https://academia.stackexchange.com/q/115231 https://academia.stackexchange.com/q/115231), and probably needs to die given alternatives like ArXiV and OpenPeerReview and etc. I'm not sure how much I care about AI slop contaminating an area that already might be mostly human slop in the first place.

jltsiren 10mo ago
That's a wrong way of using AI in peer review. A key part of reviewing a paper is reading it without preconceptions. After you have done the initial pass, AI can be useful for a second opinion, or for finding something you may have missed. But of course, you are often not allowed to do that. Review copies are confidential documents, and you are not allowed to upload them to random third-party services. Peer review has random elements, but thats true for all other situations (such as job interviews), where the final decision is made using subjective judgment. There is nothing wrong in that.
- D-Machine 10mo ago
  > A key part of reviewing a paper is reading it without preconceptions I get where you are coming from here, but, in my opinion, no, this is not part of peer review (where expertise implies preconceptions), nor for really anything humans do. If you ignore your pre-conceptions and/or priors (which are formed from your accumulated knowledge and experience), you aren't thinking. A good example in peer review (which I have done) would be: I see a paper where I have some expertise of the technical / statistical methods used in a paper, but not of the very particular subject domain. I can use AI search to help me find papers in the subject domain faster than I can on my own, and then I can more quickly see if my usual preconceptions about the statistical methods are relevant on this paper I have to review. I still have to check things, but, previously, this took a lot more time and clever crafting of search queries. Failing to use AI for search in this way harms peer review, because, in practice, you do less searching and checking than AI does (since you simply don't have the time, peer review being essentially free slave labor).

zeofig 10mo ago

This is because peer review has become a bullshit mill and AI is good at churning through/out bullshit.

TomasBM 10mo ago

The reasons listed in TFA - "confidentiality, sensitive data and compromising authors’ intellectual property" - make sense to discourage reviewers from using cloud-based LLMs. There are also reasons for discouraging the use LLMs in peer review at all: it defeats the purpose of peer in the peer review; hallucinations; criticism not relevant to the community; and so on. However, I think it's high time to reconsider what scientific review is supposed to be. Is it really important to have so-called peers as gatekeepers? Are there automated checks we can introduce to verify claims or ensure quality (like CI/CD for scientific articles), and leave content interpretation to the humans? Let's make the benefits and costs explicit: what would we be gaining or losing if we just switched to LLM-based review, and left the interpretation of content to the community? The journal and conference organizers certainly have the data to do that study; and if not, tool providers like EasyChair do.

deleted 10mo ago
[deleted]
pastage 10mo ago
When you measure something people will start to game it. CI/CD needs a firm hand to work.
conartist6 10mo ago
Better to completely drop review, don't you think? LLMs simply don't do science. They have no integrity. There is no "bullshit me" step in the scientific process
i_am_proteus 10mo ago
Yes, there are often strong reasons to have peers as gatekeepers. Scientific writing is extremely information-dense. Consider a niche technical task that you work on -- now consider summarizing a day's worth of work in one or two sentences, designed to be read by someone else with similar expertise. In most scientific fields, the niches are pretty small, The context necessary to parse that dense scientific writing into a meaningful picture of the research methods is often years/decades of work in the field. Only peers are going to have that context. There are also strong reasons why the peers-as-gatekeepers model is detrimental to the pursuit of knowledge, such as researchers forming semi-closed communities that bestow local political power on senior people in the field, creating social barriers to entry or critique. This is especially pernicious given the financial incentives (competition for a limited pool of grant money; award of grant money based on publication output) that researchers are exposed to.