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                                The Opportunities and Threats of AI for Scholarly Publishing!
                            
                            
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                                The Opportunities and Threats of AI for Scholarly Publishing!
                            
                            
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Segment:0 . 
 Hi, everyone.  Thank you for joining us.  We're just letting folks gather  and we'll be getting underway  momentarily.    
Hello, everyone.  Good morning, good  afternoon, good evening.  Depending upon where you are.  We're just letting folks gather,  and we'll get underway shortly.   OK we'll give it  about 20 more seconds,  and then we'll get started.    
OK Thank you.  And welcome to today's SSP  webinar, the opportunities,  and threats of AI for Scholarly  Publishing, understanding  external factors, business  opportunities, and use cases.  Before we start, I want to  thank our 2025 education  sponsors, access  innovations and Silverchair.  We are grateful for  your support as always.  My name is Lori Carlin.  I am the chief  Commercial Officer  at Delta Inc and the SSP  education committee's webinar  lead.   
Before we get started, I have  just a few housekeeping items  to review.  Attendee microphones have  been muted automatically.  Please use the Q&A  feature in Zoom  to ensure questions for the  moderator and panelists can  be viewed.  You can also use the chat  features to communicate directly  with other participants  and organizers  to send chat messages to  everyone in the session,  select everyone from the  two dropped down instead  of the panelists.   
I think we've all gotten  kind of used to this,  but every once in  a while I. I do  the panelists instead  of the everyone  closed captions  have been enabled.  If you don't see the CC  icon on your toolbar,  you can view captions by  selecting the More option  on your screen and choosing show  captions in the dropdown menu.  This one hour session  will be recorded  and available to registrants.   
Following today's event.  Registered attendees will  be sent an email when  the recording is available.  A quick note on SSPs code of  conduct and today's meeting.  We are committed to  diversity, equity  and providing an inclusive  meeting environment that  fosters open dialogue and the  free expression of ideas free  of harassment, discrimination,  and hostile conduct.  We ask all participants,  whether speaking or in chat,  to consider and debate  relevant viewpoints  in an orderly, respectful  and fair manner.   
Now, I'd like to  briefly introduce  today's moderator, Dave Myers,  CEO of data licensing alliance.  Dave is a serial entrepreneur  and a recurring revenue,  licensing and B2B information  expert with over 30 years  experience specializing  in strategy,  sales, legal, licensing  and business development.  He has drafted,  negotiated and closed over  500 domestic and international  licensing agreements  with partners, customers  and distributors.   
He's also negotiated and closed  countless business alliances,  strategic partnering and  revenue generation deals,  and prior to starting  his consulting practice,  he was executive  director, global licensing  and business development  with Wolters Kluwer health  for seven years.  And Dave has been an  active participant in SSP  for many years.  And so we're happy to  have you hosting today.   
Take it away Dave.  Thank you Lori.  I really appreciate it.  And very kind words.  So today as Lori mentioned, the  webinars, the opportunities,  and threats of AI for  Scholarly Publishing,  AI and scholarly publishing  presents both challenges  and opportunities.  And while concerns about  copyright and ethical usage  are valid, the potential for  AI to enhance efficiency,  personalized research  experience, and support research  is undeniable.   
And so today, you'll hear  about the current state of AI  and the possible impact on our  scholarly publishing community  with respect to external  influences, such as copyright  and future legislation,  licensing and business  opportunities, and use cases  with end users and researchers.  So AI is having a  defining moment.  Over the last few weeks,  there's been countless reactions  to us vice president  JD Vance's speech  at the Paris eye Action Summit,  many of them understandably  focused on Vance's  forceful framing  of the US as the  global leader in AI  and the Trump administration's  visions for fostering AI.   
But underneath the bluster  was something more interesting  positivity.  Vance described AI as one of  the most promising technologies  in generations, highlighting  how it can augment  rather than replace, workers.  Regardless of your political  beliefs and feelings  about the current  US administration,  the speech was correct on at  least one significant point.  We can't just fear AI or  hope it magically works out.   
We need to think  clearly about how  we want to use it in  order to start preparing  the next generation for AI era.  We have to actively  engage with this rapidly  developing technology  and create incentives  to encourage students,  researchers, and of course,  the publishing community to  achieve something meaningful.  There's been a recent blitz of  new product releases related  to AI.   
Google expanded Gemini two  OpenAI, shared his roadmap  about the future products,  aiming for a future where I just  works and interestingly, just  yesterday incorporated dall-e,  their image generation AI  into its core offering.  And of course, of relevance  to the SSP community,  just to name a few.  Humm launched alchemist  review, an AI powered tool  designed to enhance efficiency,  consistency and quality  of the peer review process.   
Silverchair and oup jointly  announced the launch  of Oxford academic AI  discovery assistant,  which supports researchers to  develop highly relevant search  results, and EBSCO  launched AI insights,  which generates a short list  of key insights for full text  articles and overall  search accuracy.  McKinsey the publishing.  Excuse me.  The consulting company  published a report recently,  and they said that  the value of I  comes from rewiring  how companies are run,  and that includes organizations  in the scholarly publishing  community.   
Their latest survey showed that  out of 25 attributes tested  for organizations  of all sizes, not  unexpectedly, the redesign of  workflows and the exploration  of AI to create new  products and services  will have the biggest effect  on the organization's ability  to see impact from the  use of generative AI.  Many organizations are  ramping up their efforts  to mitigate generative  AI related risks.  More respondents were more  likely than in early 2024  to say their organizations are  actively managing risk related  to inaccuracy,  cybersecurity security  and intellectual  property infringement.   
Three of the j'en AI related  risks most commonly having  negative consequences  for their organizations.  The practical result  of all this is  that the individual  will continue to become  more capable than ever before.  You can use AI to  generate fully COVID apps,  bring projects to life that  once required entire teams,  or even push forward on  cutting edge research.  The question is which problems  we aim all this energy at  and the vision that  guides us as a consultant  for many large and small  organizations who both license  and are licensees  of data for AI.   
I'm in a position that  affords me a bird's eye view  into how AI is being  deployed in solutions,  and how people use AI in  the course of their work.  When I work with teams  of senior executives  to create AI policies  for their organizations.  We recognize that the  most effective route  would align the fundamental  values of the organization  on which it is built. So what  do we want to happen next?  That is the core question  you all should think about,  and I'll start ending up with  a little quote that says,  I might let us do  more than ever,  but it won't decide for  us what's worth doing.   
So now I'm going to turn  to our esteemed panelists  to give you a glimpse from their  perspective of the opportunities  and threats they  see related to AI.  On our panel, we have Keith  kupferschmid from Copyright  Alliance, Kate  Whitlock from outsole,  Richard Bennett from harm, and  Avi stayman from skywriter.  So first up, Keith Coopersmith  from the Copyright Alliance.   All right.   
Thank you, David.  Hopefully everyone  can see my slides here  and very happy to be able  to present to you all.  And I think my job here is  to set the table a little bit  in terms of what we're  going to talk about,  to provide some legal  and policy background.  And before I get to  that, let me just  mention that who the  Copyright Alliance is.  So you have some  perspective here.   
We are really the unified voice  of the copyright community  and our job.  My job is to promote and  preserve the value of copyright  and to protect the rights of  creators and copyright owners.  And we do that through  several different means.  We work with policy  makers, whether it's  members of Congress,  members of the,  you know, Trump administration,  the US Copyright Office,  other executive branch agencies.   
We filed briefs in federal court  to try to educate the courts.  And a lot of what  we do is education,  which I'll talk about at the  very end of my few minutes.  Here we do represent, you see,  a smattering of our members  up on the slide here.  We do represent the copyright  interest of over 15,000  different organizations across  a spectrum of copyrighted  copyright disciplines.  So it's the usual that  you might think of when  you think of copyright, right?   
Movies and record labels  and book publishers  and video game publishers  and scholarly publishers,  I should mention as well.  But it's also groups  that you don't  think about, like  sports leagues,  the National  Association of Realtors  with their MLS database  that's protected,  or the National Fire  Protection Association, which  has got electrical codes and  that are protected by copyright.   
And then we also represent about  2 million individual creators.  These are the photographers  and the performers  and the songwriters  and the software coders  and the artists and the  authors and this new generation  of creators that are out there,  many others who create and make  a living through  their creativity.  So when it comes to AI, you  know, we talk to all of them.  And, our general view.  Their general view  is that we support  responsible, respectful and  ethical development and use  of AI technologies.   
And and that means,  you know, that  means respecting  copyright on obviously.  So what I'm going  to talk about today  is I'm going to go  through fairly quickly,  though, the current  state of play.  I'm going to talk about  government AI activities,  both legislation in  the United States  and abroad, the Copyright  Office, what they're doing,  the Biden and Trump  administrations, what  they're doing, the courts.   
Boy, Oh boy, are they busy.  And then sort of wrap it up  with licensing technology.  And and do all that in  just a couple of minutes.  So I'm going to kind of fly  through here a little bit.  So first off government  AI activities.  So in terms of congressional  activities frankly  and maybe this shouldn't  come as too big a surprise.  There hasn't been  too much right.  Congress lately  just doesn't seem  to do very much in  any particular area.   
But they are obviously  concerned and looking into AI.  There have been many, many  congressional hearings  on the issue of copyright in AI  and obviously AI more generally.  And then late last year, the AI  taskforce put out in the house,  put out its report of views, and  then copyright, in essence said,  we're looking to the courts.  The courts are going to solve  these problems for the most  part.  The one area where there  was copyright legislation  in the United States  that was considered  is on transparency and  transparency bills,  and they were  introduced last year,  so they would have to be  reintroduced this year  since it's a new  session of Congress.   
And in essence, what  these bills would do  is require AI companies to  disclose the copyrighted works  that they ingest for  their training purposes.  So and that's all it does.  It doesn't require weights  or parameters or algorithms  or any of that stuff to  be, to be, to be disclosed.  Just the type of the works  that are actually ingested.  And there are two different  approaches to that.  One was in the shift  Bill, which at the time  was introduced in the House.   
Of course, Representative  Schiff is now Senator Schiff,  but he introduced a Bill  that would require all AI  companies to really deposit that  information, all the copyrighted  works into one depository.  And then there was  the Welch Bill.  Senator Welch from Vermont,  who had introduced a Bill that  would basically require AI  companies to disclose what  copyrighted works they're  using if they were  asked by the copyright owner.   
And so we do anticipate  that at least the Welch Bill  will be reintroduced.  I'm not sure about the  shift, the shift Bill or not.  And then there's legislation  outside the United States,  and we have the EU  AI Act out there  that would require  transparency and opting out.  But it's very much even  though it's in force.  It's still very much  a work in progress.  I did leave a lot of the  details to be figured out.   
And and we constantly see  kind of updates there.  There's a new consultation going  on in the UK about a proposed  exception that would  allow a copyrighted  works to be used without  compensation or authorization.  And the creative community  has really, really  pushed back on that.  And then there's  other countries.  Singapore, Japan,  Hong Kong and others  that have considered  what to do here  in terms of AI and  copyright and in some cases  actually have exceptions.   
In some cases, the idea  of those exceptions  have been rejected in terms  of presidential action.  President Biden put out  an executive order on AI.  I will not talk about  that because that doesn't  exist anymore, because when  President Trump came on,  one of the first things  he did is basically end  that executive order terminated.  It would have required the  Copyright Office and the patent  office to work  together to come up  with some recommendations  on copyright in AI.   
The Trump administration  itself has put out a request  by OSTP for information  on various AI issues,  including copyright and those.  Those comments were due back,  I think it was on the 25th  of March.  And so they are now calling  through those over 8,000  responses to try to figure out  what their AI action plan should  say.  And so we'll be  waiting for that.   
I don't know when  that will come out.  The Copyright Office  has been very active.  They've put together 3  studies, two of which  have already been released.  One is on digital replicas.  The idea of someone protecting  their image, voice and likeness.  Another was on copyrightability.  And that was published  in January of this year.  And then we expect  one more, which  is on ingestion, fair use  and allocation of liability,  which will be coming soon.   
So I would certainly keep  your eyes out for that.  And happy to talk about any  of these in more detail.  If people are interested.  But the real activity, the real  big activity is in the courts.  There are about 40  different pending  AI copyright infringement  cases in the court.  About half of those  are class actions.  Every day it seems like there's  new activity in these cases.  Matter of fact,  this morning there's  a bunch of activity in  some of these cases.   
The majority of these cases  involve authors and publishers,  right.  The vast majority.  There's a couple cases  involving music, photographs,  you know, images, computer  code and what have you.  But almost all of them have to  do with literary works, authors  and publishers filing cases.  And most of the cases  are brought or pending  in California and New York.   
And the primary issue  in all these cases  is, is the unauthorized  copying and/or  ingestion of copyrighted  works for training  purposes of fair use.  Because it's fair use, the  AI companies can do it,  and they don't have  to compensate anyone,  and they don't have to get  authorization to do it.  And so fair use is decided  on a case by case basis.  There's no one rule.   
You have to look at each case,  look at the facts of each case  and decide.  AI companies will make  the point that they think  their use is transformative.  But in each case, you  have to look at that  and go, OK, what is the ultimate  purpose and justification?  If you've got a generative  AI program that's  ingesting visual  works or music, let's  say, to produce  other music, that's  not terribly transformative.   
That might be  substitutional in nature.  And that's a problem.  What is the impact of scraping  pirate websites should, you  know, should that be  protected, really,  that the fact that it's pirated  works and they're scraping that?  What harm is there to  the licensing markets?  Frankly, that's the big  issue because there's  a lot of companies  out there that  are licensing their works  to AI companies to ingest.   
And if all of a  sudden it's fair use,  those licensing  markets disappear  with the blink of an eye.  That is a big, big issue.  Only one of these  cases has been decided.  That's Thomson  Reuters versus Ross.  That was decided  just a few weeks ago  actually in February in 2025.  And in that case, the  court held that it was not  fair use that what  Ross was doing  was an infringement of Thomson  Reuters copyrights in their head  notes.   
And so but that's just one case.  And like I said, all these  cases are decided on a case  by case basis.  So let me wrap up  here a little bit  in terms of the future  of AI and copyright  and what to watch for next.  Like I said, there are  about 40 cases pending.  There are new cases,  it seems like filed  every week or every month.   
And so there'll be new  cases to watch out for,  but there'll be new cases  coming down and being decided.  So that will be  something to track.  The Copyright Office report  will be coming out presumably  in the next month or two.  And this UK  consultation, what the UK  decides to do on these very  important copyright issues.  They don't have the fair  use doctrine in the UK  or for that matter,  anyplace else.   
So whether they decide to  create an exception or not  will be interesting in our mind.  The solution here is licensing,  licensing, licensing and also  technology, of course.  Right and if we just put  everyone into a room,  I think we'd be able to  come up with solutions.  And let's not have to rely on  Congress or the administration  or anything like that.  So just real quick in closing,  in closing, I think I just lost.   
There we go.  Is if you want more  information on any of this,  I encourage you to  go to our website.  We've got this page, which you  see up on there about education.  We've got FAQs.  We've got a special page  on artificial intelligence  that you can find a whole  bunch of information on here.  And then also under  our get involved  page, which is all the way to  the right, the orange part,  we actually have  different newsletters.   
And one of our newsletters, even  though it's not shown up here  right now, is the we  have an AI newsletter.  So if you're interested  in just keeping up  with what's going on in AI, I  encourage you to check that out.  So with that, I will throw  things back to David Keith.  Thank you very much  for those words.  Up next on our panel is  Kate Walsh from outsole.  Hi, Dave.  Hi, everybody.   
Thanks for the intro.  I don't have any  slides, so you're  going to have to just  watch my face or, you know,  don't watch my face and  go and do something else.  But listen, there are  interesting things coming up.  So what I want to talk about  today as part of this webinar  is what we've seen scholarly  information providers  doing with AI.  Some of the low hanging fruit  that we've seen being picked  and where we think this  might be going next outsole  provides market research,  strategic analysis  to information providers across  the information industry,  as well as to financial services  and technology providers.   
We've been looking at  Gen AI extremely closely,  obviously, to the extent  that some clients at one  of our recent conferences, we're  like, is there not another topic  that we could be  talking about as well?  And of course, there is, but  this has been really dominant.  I think what's important  to remember, though,  is that AI isn't  new to providers  of scholarly  information resources.  So we're seeing the next  generation of offers  as generative AI  has come into play.   
And it does.  I think what's  important, though, is  that AI does enable us to  do things which have never  been possible before in terms  of solving some researcher  and end user pain  points so we can help  to improve working practices.  And that's both within  scholarly providers  and for researchers themselves.  We can help researchers to  easily scour vast reams of data.   
And we all know about the amount  of publications being created  and the amount of content that's  out there to sift through.  We can help them to  identify relevancy  in a really efficient manner.  And that's  particularly important  when they're looking at  unfamiliar research search  areas, we can help  with the summarization  of huge volumes of data.  We can help to  identify trends using  AI in really vast data  sets, particularly  through visualization.   
And we can even help users  to understand and access  content which may not be  in their native language.  So lots of things  that AI is able to  is able to speak  to that we haven't  been able to look at before.  So I've narrowed  down a few use cases  that we've seen  publishers speaking  to when they've launched some  of their new Gen AI offerings.  I think one thing that's  important to notice  is that there's a number  of ways in which these  are being charged for as well.   
So I want to come on  to talk about some  of the business models a  little bit later on too.  But let's talk about  the actual capabilities  and use cases first.  So I think that that first  piece of low hanging fruit  was really around search.  And how can we improve search  using semantic tools and AI.  But early AI tools were in  were in that space for years.  So on silo, for example, from  cactus, was used by project muse  to drive content  recommendations.   
So once OpenAI launched  ChatGPT at the end of 2022,  we saw new types of solutions  emerging pretty quickly.  So by September 23rd, Elsevier  had started piloting Scopus.  AI Digital Science had a  beta test of its dimensions.  AI assistant and site had  launched site assistant.  You'll see the word assistant  coming up a lot in generative AI  tools from scholarly  information providers,  and I think that's really a  key way of helping providers  to emphasize that they're  looking to support  and facilitate the work  that researchers are doing,  not to replace those  activities in any way.   
I think what we also saw  right at that beginning point,  when some of these new search  tools were being introduced,  introduce was sort  of a note of caution.  There was worry about validity  of these generative AI search  results, worries about  things like hallucinations.  And to honest, that's a problem  which still hasn't gone away.  So different  technological approaches  were taken using more  traditional search  methodologies, for  example, to pull out  a small number of articles, or  over which the generative AI  technology can then be  run to produce results.   
That's certainly what.   Happened when solutions started.  And actually it's an approach  that continues to this day.  So the new Science  Direct AI product  that came out a couple of  weeks ago, what that does  is it uses a sort of a  rag method of searching.  They call it a chunking method.  It must be called something  more technological than that.   
But I don't know what it is, and  it pulls out relevant passages  from full text  books and journals,  and then they search over  that more limited data set  using the generative AI tools.  There's lots of startups  in this space as well.  I mean, I've mentioned  some of the bigger players.  But we've seen illicit,  we've seen consensus,  we've seen zeta alpha.  There's a number of  providers out there who, who,  who are doing some really  interesting things.   
And they've added some  additional capabilities  into those initial  search offerings.  So Clarivate web of science  research assistant as their word  assistant.  Again, you see, offers natural  language search capabilities  in several languages.  So that speaks to  that pain point  of not being able to  access content outside  of your native language.   
And the new Elsevier  Science Direct AI tool  has a reading assistant, which  essentially enables researchers  to chat and have a conversation  with specific journal articles  and to query that article  through a chat functionality.  So it's still search,  but it's a different way  of looking at that  search problem.  So search was sort of  the first big use case  that I wanted to talk about.  I also wanted to talk about the  way in which these tools help  to sort of supercharge  our research workflow.   
So search and  discovery is really  just the start of a much,  a much longer process  of research activities.  So that might be things like  automating literature reviews,  generating hypotheses, drafting,  undertaking editing, even  some peer review.  And I'm sure  Richard will come on  to talk about the kind  of peer review activities  that hum have been working on.   
Again, lots of startups in this  space elicit again and research.  Rabbit and I think  what we started  to see after a few months was  that researchers were starting  to understand that there were  really interesting capabilities  out there from, from,  from generative AI tools,  but they were kind of  using them in the same way  that they were using  old type of tools.  So search is search.  It may be a more  effective search,  and you may start to evolve  that through a kind of a chat  mentality.   
But it's but it's just search.  And they were finding  it difficult to think  about other use cases and  other ways in which they  might use these tools.  So we started to see providers  building in guidelines,  as it were, I guess.  So so Clarivate started with  its web of science research  assistant, and it had  context specific prompts.  So rather than just having a  search box when the user arrived  at the page, it's got a  box saying something like,  are you trying to find a  journal in which you might  want to publish something?   
Or are you trying to  understand a topic better?  Or are you undertaking  a literature review  and helping to guide through  those kinds of questions.  And prompts to get really  effective usage of those things?  And we've seen that  evolving as well.  So elsevier's new Science  Direct AI product, one  of the things that  they noticed was  that one of the most time  consuming tasks for researchers  was about identifying  methods and protocols that  had been used.   
And comparing those methods  across multiple journal  articles.  So they've produced a tool that  does a sort of a generative AI  summarization over a  certain set of articles,  but looking  specifically at methods.  And then it summarizes  that in a grid.  So it's a bit like when  you're going to Amazon  and you're buying a  new vacuum cleaner,  and it has a grid with  the different capabilities  and the prices, and you're able  to really compare very simply.   
So it uses that same  kind of approach.  They describe that as  really being a wow factor.  People haven't  seen that elsewhere  and they saw it  as a massive time  saver, which was really  important for researchers.  There's a number of  different other use cases.  I don't want to take  up too much of my time  because we've got some other  fantastic speakers here,  but I think we've seen the  use of data visualizations  to improve research  capabilities.   
We've looked at the  way in which generative  I can help with confirming  transparency and stability.  So our repeater, which is a  division of Digital Science,  uses natural language processing  to look at the content of papers  and look for hallmarks of  sort of responsible science.  And then in a more  applied setting,  we've seen the use  of generative AI  to help facilitate  clinical decision support  and accelerate R&D  in pharma contexts.   
I actually did a  piece of research  towards the end  of last year that  looked at what researchers  might be expecting in terms  of future use  cases, and I thought  that that would be  useful to highlight here.  So about half of  them were hoping  to use generative AI to generate  creative ideas or designs.  So in other words,  to use it right  at the start of the process,  which is really interesting.   
2 2 actually undertake research.  So we've seen a lot of that  search activity already,  43% were looking to generative  AI to help them with drafting  of documents or reports.  37% thought they'd use it  to automate admin tasks.  I don't know why  that isn't higher.  It's definitely a very  good usage and 30% expected  to use AI to analyze data.  So there are lots of different  opportunities still out there.   
I said I'd quickly talk  about business models,  so I just want to spend  half a minute on that.  We do see tools sometimes  available free of charge.  Increasingly  unusual, I would say.  But I think at the  very beginning,  when people were looking to set  a sort of a competitive position  that was relatively  common and looking  to make up their investment  by increasing revenues  through raised prices or  improved renewal rates.   
Some startups offer paid  for standalone solutions,  but for some of the  bigger providers,  we tend to see a premium  level solution offered  at an additional cost to the  existing subscriber base.  As Dave mentioned, there are  some challenges in this market  as well as opportunities.  I feel like are focused  on the opportunity.  So I just want to close  with some of the challenges.  I think for many publishers,  there's a commercial imperative  to invest in AI  driven innovations  to help them keep  that competitive edge.   
The larger players tend to  be at an advantage here.  There are obviously  concerns, as Keith  has outlined, around  protecting IP,  and there are also concerns  around research integrity.  We're using generative  AI now to try  to identify content created by  bad actors using generative AI.  So the battle is  certainly on there.  I've certainly heard  a lot of concerns  from librarians around  hallucinations and information  quality.   
And they're very keen to  test solutions before they  get to their patrons.  In some cases, they  may be more concerned  than patrons themselves.  I think it's natural for  researchers and health  professionals to very  much sanity check  the research that they find and  to validate it by, by comparing  with other resources.  So hopefully there won't be they  won't be a bottleneck because I  think there's a really  interesting opportunities  and values here for a lot  of scholarly researchers  and for the scholarly  publishing community overall.   
It's quite a high  level overview,  but those are my  initial comments.  Happy to answer any questions  when we get to the Q&A session.  Thank you Kate.  Our next panelist,  Richard Bennett from hum.  Thank you, Dave.  So for those of you who  aren't aware of hum.  Hum is a technology company  based out of Charlottesville  in Virginia.   
We've been active in  the scholarly publishing  space for around  about four years now.  So I'm going to share  with you a couple  of maybe more practical examples  of the utilization of AI  in scholarly publishing.  You've heard from quite a  few from the landscape side,  but these are going to be  looking at two different aspects  where it really does have  a benefit for us to be  able to utilize it.   
One is around audience  intelligence and the next one,  and thankfully it's  already been mentioned.  But the more recent application  of AI in editorial stroke  peer review kind of processing.  So just a little bit about  of our backstory, and it  kind of gives you an idea  of where we developed  and why we developed into  the kind of AI space.  So hum started life as a  customer data platform.  So aggregating pieces  of disparate pieces  of data across a  publisher's ecosystem  to create a single record.   
The one thing that became  very obvious quite early on  was that there was a huge gap  in publishers understanding,  or at least the data  around the interactions  that a publisher will have  on their content site.  And the content  site, obviously is  providing one of the key aspects  of integrated interaction  with the research community  as they're reading.  But most of these  users are anonymous,  anonymous profiles that but  but those anonymous profiles  were engaging deeply  with the content.   
So one of the things that  hum really needed to do  was to find a way of being  able to create a way of,  of understanding these  profiles and create  an intelligence around it  that would allow publishers  to both understand more deeply  and also be able to activate  on these interactions.  So hum developed.  Alchemist alchemist  was is a language model  that was based on a  model called lodestone.   
And lodestone was  essentially developed  to be able to cope  with a longer queries  around longer strings of text.  So obviously with scholarly  publishing and research  articles, you have a very  text dense situation.  So essentially you needed a  model that could cope with that  and be able to understand that  in a significant and deep level.  So the way that the AI kind  of aspects come together  is really around the  content intelligence aspect.   
So taking in a  publisher's corpus  and then being able  to use a couple  of different versions of AI to  be able to create a taxonomy.  So it's building  a custom taxonomy  by using interpretive AI to  be able to extract things  like topics from the content,  and then generative AI to create  structure and organization.  So multi-level taxonomies  from that, from those topics,  once those that taxonomy  is created and those topics  are attached to pieces of  content on the content site,  you can start being able to see  how different profiles interact  with that content.   
And those topics are essentially  will then migrate over  to the profile,  and it'll give you  a profile of actually  topical interest,  but also of the level of  engagement that you have.  So there's a secondary basis.  You can actually start  looking at that audience in a,  in a much deeper, deeper level.  So you can actually have  this AI native understanding.  So we can look at profiles  and understand and see  what they've interacted with,  how much they've interacted  with that, that content.   
And that gives  you a basis of you  may not have any demographic  kind of details of that user,  but you will know them deeply.  You will know what they're  interested in, how much they're  interested in, how  recently, they've  been interested  in, which gives you  a very kind of rich  set of data that you  can be able to utilize for  various different use cases.  The other side is the  predictive intelligence.   
So by actually having  a mass of data,  you can actually start being  able to understand not just what  people have done, but also what  people might do in the future.  So for example, if  you have a profile  and then you want to be able to  understand where it might want  to go next, what piece of  content might be interested,  you can look at the collective  understanding of all  the profiles who have a  similar, similar kind of route  through the data  and then see what  is the most engaging  piece of content  that they went and found  after that, that point.   
So you have a whole  host of number of,  of actionable usage that  you can be able to build off  the back of this.  So content recommendations,  content recommendations up  until this point have  really been contextual.  So they've been associated  with the piece of content  on the page.  But what this really actually  allows you to start to conceive  is that actually you can make  a content recommendation based  on the profile and the what you  understand about the profile,  and be able to  provide and surface  content recommendations  that are tailored  to that individual person.   
You can do interesting  things, like you  can take a scope  of a special issue,  and you can put it  into a model, and being  able to have the  topics extracted  from that, from that  scope, be able to search  your entire database,  find the exact  matches that have the same  level of deep engagement  with those topics.  And then be able to invite them  for authorship opportunities.   
So there's so you can be able  to talk very specifically target  authorship opportunities  to individuals.  And the same thing  for advertising.  It's similar to the  content recommendations.  It has tended to be  contextual in its purpose.  So but by being able to move it  to a behavioral based targeting  Association, you can be able  to open up a far greater level  of inventory across your site.  You're not linked to just  having content that could  be advertised on a single page.   
You can advertise  to whoever might  be interested in  your target group,  wherever they're occupying  in your content sites,  so you can be able to have a  far wider range of opportunity  to be able to  interact with them.  So this is the kind of shift  that we made as the next shift.  We had a very deep understanding  of content, and in many ways.  We started to look at what are  the opportunities out there  in the sky, or the challenges  that scholarly publishers were  facing, and where could we?   
Where do we feel that we could  bring that content intelligence  to bear in solving some  of the key problems?  And one of the key  problems, obviously,  is that the submissions are  growing at an exponential rate.  You have a limited pool of  editorial staff and peer  reviewers who are trying to  process those manuscripts.  There is a greater  level of scrutiny  to each of the manuscripts  as we kind of work forward.  So one of the aspects  that we decided to look at  was, can we start extracting  from research publications  interesting pieces  of information that  will help editors  in the first stage,  but also potentially  into peer reviewers  do their job in a more  efficient, more consistent way.   
Alchemist review, obviously,  is a beta launched.  It's currently under development  with a few different publishers  at the moment.  But what this does is a  number of different things.  Essentially, it does kind  of three major actions on a,  on a manuscript.  The first is the  content extraction.  So this is, you know, what  you would normally have.  So extracting the key  interesting parts.   
And you've heard a  little bit from Kate  on some of the, some  of the bigger models,  the, the bigger sciencedirect  of this world who are starting  to do this sort of work.  But it's things like creating  a really entity dense expert  summary.  So can you distill the  meaning and understanding  from a piece of research into  an eight sentence summary?  It's extraction of  the author claims.   
Can we extract and  present the claims  that the authors  are making on why  their research is valuable and  valid, and should be reviewed?  Extraction of key  concepts and things  like the research methods.  So again, it's  that these aspects,  these key aspects of the  research paper being able  to extract them.  I think one of the really  interesting aspects  at the moment is that there are  now these deep thinking models.   
So these are not just  immediate response models,  but these deep  thinking models which  allow us to be able to do  much deeper analysis on pieces  of research.  So that allows us  to be able to look  at statistical kind  of applications.  So are the sample  sizes congruent  with the statistical kind of  methodology within the paper?  You can start making  these sort of assessments.   
You can start  looking at novelty,  whether from a discipline or  a methodological application  within a discipline  specific perspective.  Writing quality image analysis  from a contextual perspective.  So is the image  congruent and relevant  for the context that it's  been presented in the paper.  And look at  methodological issues  within that may occupy within  the actual paper itself.  Finally, you can start doing  things like structural addition.   
So applying taxonomic  terms at submission,  you can classify the paper  automatically at submission.  And you can start looking at  retrieval of related content.  So there's a whole host  of different applications.  What I mean essentially  what it's doing  is creating an environment  hopefully where  a lot of the key significant  work, not the thinking  work, and maybe  not the contextual  analysis in the  field, but actually  the actual thinking  work on the manuscript  itself is done for the  editor or the peer reviewer,  and then you can be able to  essentially have them focusing  on the research itself.   
 I thought I'd put  this in because I  don't think we're really going  to be taken over by robots,  I think.  And you've heard you heard  this all the way with Kate's  presentation as well.  The future really  is looking at how  we can apply AI to  make greater efficiency  and support the  human based process.   
And I don't think certainly  for the near term,  we're really looking at  a future where that's  going to change significantly.  Where we can change is we can  make that process massively more  efficient, massively  quicker and probably more  consistent in its nature.  So that's and happy to  take questions at the end  when we're in the panel.  Fantastic Thank you Richard.   
Our last speaker is Avi Steinman  from academic language experts.  Brilliant Thanks so much,  Dave, and pleasure to be here.  So I'm going to share  some observations  from having traveled around  European universities  for the last year and a  half teaching them about AI.  I teach both researchers  and research offices,  and I get bombarded  with questions about AI.   
So I want to share with  you, the publishers, some  of the questions that they  asked me about publishing  and publisher policies and  some of the lackluster answers  that I have to give them,  because I think there's  some work that we have to do as  an industry to really make sure  that researchers are that  we're not over legislating  proper and good use of AI or and  potentially under legislating  potentially bigger issues  that could come about.  So first of all, just share  some of the biggest, you know,  kind of themes that come up  around when I do my teaching,  when I'm actually  in the classroom  and go to the universities  and talk to them,  I would characterize  researchers approach  to artificial intelligence  as cautious enthusiasm.   
There's a lot of  excitement, as was discussed  by some of my colleagues.  Researchers are they're  so kind of inundated  with between their  teaching and advising  and Grant submitting  and, and, you know,  proposal writing and  article submissions.  They will look for ways and  means to become more efficient.  Time is their most  valuable asset.  So if they can rely on  the AI, they will do so.   
Whether or not, by the way,  we say that they can or not.  That's just something  that's important to note.  They're cautious because  they're scientists.  And I think that most of  them with good intentions  are really looking  to do the right thing  and don't want to be producing  research that is lacking,  that is substandard.  So, you know, they actually self  I would say they self regulate,  to a great extent.   
Second thing I see are big, big  issues around privacy right.  So two there's two kinds  of privacy questions.  The first question that's  almost always asked  is my if I, you know, share my  information with the AI tool,  not so much.  Is it going to be as the AI  tool going to be using it?  But it show up  somewhere else online?  And that's a point of  caution for these researchers  is what's going to happen  with this research.   
Who's who's taking it, who's  using it, who's seeing it.  And it's not a one  size fits all answer  in terms of the different tools  have different privacy levels.  Sometimes within  the tool there's  higher levels and lower levels.  So I try and show  and demonstrate  how to be private  online, but I think  it's important to know  that that's there.  I also saw a question  about that in the chat,  and I'll say that it's not  an impossible challenge  to overcome.   
It's not an inherent  technological limitation.  Meaning it's very simple.  It's quite simple, actually,  to set up a via an API  or on a private server to run  some of these large language  models where nothing where  everything's entirely private,  it's just a matter of  how things are set up.  And then the last  thing that I'll mention  is they have a strong  preprints for tools that are  built around trusted sources.   
So, you know, when I,  for example, if you use,  deep research tool  of ChatGPT, you  may get a mix of academic  and non-academic sources.  Whereas when you use tools that  are dedicated towards research  based and are built on  Semantic Scholar and PubMed.  So Kate mentioned a bunch of  them earlier PSI, PSI space,  you know, and others,  they are built on top  of the academic literature.  So that's important  differentiation to make.   
The big issue is,  is that they really  cover Semantic  Scholar essentially  covers the open access  literature and only  abstracts from the  non-open access literature.  So there's still this  big gap where basically  imagine you're, you  know, you're coming  and you're asking a question.  And the answer is only based  on open access literature,  which in certain cases is  more than enough, right?   
If you're asking a pretty simple  and straightforward question.  But if you're asking  a very kind of niche,  high profile question,  the fact that you're  missing 50% of the research  is actually quite critical.  So, you know, I think  the next generation,  what we're going  to see hopefully,  and what I'm hoping  for is that we're  going to see licensing  deals struck between some  of these tools, these search  tools, and the publishers,  so that when a researcher  goes and asks a question,  they're actually getting  a complete, verifiable  but also comprehensive answer.   
Yeah so currently there's  a few frustrations  that researchers  have with publishers.  And I think it's important for  publishers to be aware of this,  because these are kind  of the questions that  come up in my, in my trainings.  And these are the things that  I've been pushing and trying to,  in different fora.  I've written in the Scholarly  Kitchen, the Digital Science  blog about these topics and how  we as a publishing community  can do a better job supporting  researchers with AI.   
So first of all, is it's really  unclear about what we all  talk about.  Declaration so almost  every publisher  has a policy that says,  well, you need to declare  and you need to be transparent,  but how exactly you do that when  you do that, where you do that  is definitely not consistent,  even across publisher profiles.  So even within WileyPLUS  or within Elsevier,  there might be  different journals  may have different rules.   
And just exactly how am  I supposed to do that.  So on a very basic level,  maybe it's enough to have,  you know, if I'm a  researcher, I may think,  well, maybe it's enough to  have one line that just says,  hey, I used AI for  parts of this project.  Whereas, for example, I'll  just give one example.  The Lancet came  out with a what I  would call a very strict  policy, whereby they basically  have stipulated  for the researcher  that they need to document and  share any time that they're  having a chat with GPT.   
Right that has something  to do with the research.  You know, I don't I don't  personally think brilliant idea,  but it's important.  But researchers can get  confused because they  say declaration doesn't  mean the same thing  in different journals.  So what do we do?  The second thing that they fear  is they fear being penalized.  They fear that US publishers.   
Maybe they think more of us  than we know about ourselves.  We have, magic tools  that can basically  go into their  computers and identify  any time they've used AI.  So sometimes they'd  love to use it,  but they fear that the  publishers are going to they're  going to get in trouble or  that publishers have banned it  and therefore they're  not going to use it.  To me, that's a shame.   
You know, if you've  got a researcher who's  not a native English  speaker has trouble writing  and communicating their  article if they're not  going to use I because  they're worried  about some punitive measures.  I think that's a shame.  The lack of guidance.  So they're looking  for publishers  to tell them how should or  should I not be using this?   
Sometimes they're looking  to their University,  but they, it's kind of this,  this circle where everyone's  like, Oh, you know, just  be tell us how you used it.  But but before that,  they're asking, well,  how should we be using it?  What is responsible use?  Are all use cases the same?  Spoiler they're not.  There's a big difference between  proofreading and data analysis.   
And we need to really  kind of get into the weeds  in order to do this properly.  And then finally like just  again, this transparency,  I'm a big fan of transparency.  And I'm all for it.  But I think the  transparency needs  to when we look through  a transparent window,  there's got to be something  on the other side.  We need to know what the  value system is or and I  think it's just reinforcing the  current scientific value system.   
I don't think we need to be  doing anything different or new.  It's important to know that AI  adoption really is very much  dependent on different factors.  I mean, of course I'm  generalizing here,  but based on the  research that I've seen,  age plays a big factor.  So much more likely that your  doctoral and post-doctoral  students are going  to be, you know,  kind of jumping all in  on AI, whereas the pis  are going to let them do it.   
You know, not going to  use traditional methods.  So there's an interesting  tension there.  Interesting to note different  geographies and their adoption  rates of AI.  So for example, I think that  if you know, in the, you know,  India and China.  And where else  have I seen brazil?  They have no qualms at all about  using large language models  they're happy to feed in.   
And again, I'm  generalizing but happy  to feed in their information.  They don't see  the issue with it.  Whereas I think  especially in Europe,  there's a lot more concern  around GDPR and privacy.  So, you know, to understand  there's that kind of adoption  gap there.  And the last thing I would say  is differences in subject areas.  So, you know, in general,  humanities scholars  tend to be more, you know,  skeptical or hesitant to adopt  these than STEM researchers.   
Obviously, computer  scientists lead  the way in terms  of their use of AI,  and then social sciences  are somewhere in the middle.  So just to kind of wrap up  some key business opportunities  that I see, you know, from  my perspective, first of all,  is peer review.  I don't think we should  be afraid of experimenting  with AI with peer review.  That doesn't mean we should hand  the keys over to AI tomorrow,  but it does mean that I think  we need to be asking ourselves,  what are the different  components of peer review?   
Break it down to its  basic foundational,  you know, atoms, as we might  say, and ask ourselves,  which one of these do humans do  better and which one of these  does I do better?  And then maybe we can  make a more sophisticated  augmented approach where  we're kind of, you know,  synthesizing between the two.  The content discovery has  already been mentioned here.  I mean, incredible tools  like undermind and perplexity  are doing really deep  research and being  able to really write out search  plans in a very similar way  that you would, you know,  you would as a research lab,  maybe even better.   
But instantaneously, which  is really quite incredible.  And I think in the end,  it's all about trust, right?  It's about validating AI usage.  You know, researchers are  looking to us as the publishing  community to say, yes, you  know, here's how it works here,  how it doesn't work.  Here's what you should be doing.  I want to just  call out for good.  Wiley, I don't have any no  conflict of interest that I  particularly.   
They just released  the first, I think,  the first comprehensive  guidelines  for AI in your writing  for researchers.  I've been very critical of  kind of publishers being  very kind of thin on their.  Yeah, just be transparent  and all's good.  And, you know, we'll sweep  everything else under the rug.  I'm not going to go through  this whole thing now,  but it is dozens of pages in  terms of not only guidelines  but also kind of best practices,  also more educational,  which I think is a good way to  go about it because, you know,  it's great to make  a rule, but people  need to understand how to apply  that rule and what makes sense.   
So I highly encourage you.  I can share this.  But again, this is  really hot off the press.  I think it was published,  a few days ago.  You know, if you're looking  to kind of, you know,  ask yourself, well, how do  we even approach this topic?  I think it's a good  starting point.  So recommend you  checking it out.  And just to  conclude, you know, I  kind of when I do these  trainings that I do, I  ask people, give me your word.   
Right give me give me your one  word that describes your feeling  around AI and research in 2024.  Last year was a lot about  fear, distrust, and nervousness  this year it's different.  It's curiosity,  learning, excitement,  potential, possibility.  So I think that really  reflects kind of this,  you know, a quick,  informal sentiment  analysis of the  research community  shows that we are changing.   
And the question that  I'm asking myself  is, is 2026 going to be the  year where people actually  take the plunge?  And not only individuals, but  institutions take the plunge.  Start investing in tools  and tool infrastructure.  Structure, understanding  the transformative nature  of artificial  intelligence, and maybe  get to a point of actually  transforming the research  process as a whole.   
So Thanks, Dave, for the invite.  Great to join you today.  If anyone wants to continue  the dialogue and conversation,  I try to write about  AI and research  on my LinkedIn page  at least once a week.  You're welcome to reach  out and connect there.  Fantastic Thank you  Avi, and Thank you  to all the panelists  for your participation.  It really warms my heart.   
I heard the word licensing in  a number of talks and you know,  that's where I focus on.  And I really think that that is  one of the largest opportunities  for the publishers on this  call, because once the Google's  and the rest absorb  all the open access  content in the world, what's  left is the firewall content.  And as mentioned,  comprehensiveness  is one of the  aspects to great AI.   
And they're going to  need your content.  So that's the lowest hanging.  Opportunity I see.  And hopefully we can,  you know, talk about it  if we have some time left.  Unfortunately we only  have about four minutes.  So we have four  questions in the AMP  and I encourage  anybody on this webinar  to include your questions,  and I'll get to them as  or hopefully we can  get to all of them.   
The first one is, how does  AI influence the selection  of references and citations?  Anybody want to take that one?   I don't know if we know.  I mean, I'd be happy if someone  has more insight on this  than not, but I haven't seen  any research that really digs  deep into, you know, if there's  a certain bias or trend towards,  you know, quoting  certain researchers  over other researchers,  there's definitely,  like I mentioned before,  there's definitely  a bias towards open access,  simply because that's  where it can search  the full text  and be able to spit it back out.   
So I think you'll  probably see an increase.  Assuming that researchers are  using these tools quite heavily,  you'll see an increased  use of open access  text, which were already,  quoted more often anyway.  But I don't know if there's any  more specific kind of biases  that are baked in  that have been proven.  Yeah, I tend to agree with you.  I think that the challenge is  it's all really around context.  And so encouraging both  authors and publishers  to have the most enhanced  metadata that they have.   
Because when the AI tools  manage the relationships  between the content  and the context  that they're looking  for, that may  influence the selection of  the references and citations.  That's the only thing I can  really think of as well.  Yeah I mean, I was just going  to give a perspective from maybe  from the opposite side,  insofar as you've got tools  like grounded AI  that are looking  at contextual kind of citations.   
And so I think the  focus there's going  to be a greater level of  clarity and focus on the,  you know, validating  the contextual use  of the site of citations  within a research paper.  So how it influences  the selection.  I mean, normally these are,  you know, the chicken and egg  type of scenario.  They're definitely  the tools are there.  And cite is another  good example.   
Right of the tools  are there that  are making the selection  of references, a part  of the assessment of the paper.  So I think so I think it  will have an influence.  I'm not quite sure  how it will be,  how it will influence just yet.  Yeah Thank you.  This next question is for Kate.  Kate, can you talk more about  the use of generative AI  in clinical decision support?   
Yes, briefly, because  we don't have long.  We've certainly seen  the use through.  There's a startup called I  think it's called aiforia.  Aiforia aiforia.  I don't know how  you pronounce that,  but they do image  analysis for pathologists  and diagnostic tasks so they can  improve accuracy, reduce bias,  standardize analysis.  So there's certainly  examples there.   
We're also seeing quite a lot of  incorporation of generative AI  into electronic health records.  So Epic did a deal  with Microsoft.  And there's a couple  of other examples  there really to try to  enhance clinical decision  support, real time insights  and recommendations,  personalizing of  treatment plans.  Speech recognition and  natural language processing  to reduce admin burden and  streamline documentation.   
So a whole range of case  studies if anyone's interested.  Just just reach out and I can  provide a bit more detail.  Thank you Kate.  This next one is for Richard.  How does using AI and peer  review fit with the best  practices around not feeding  confidential, privileged  communications into AI tools.  That's a very good question.  I mean, the first aspect  that just for clarification,  in all of these different  models and everybody  who's utilizing kind  of AI tools around peer  review, the actual manuscripts  themselves aren't being Fed  in for training purposes into  any of the actual models that  are out there.   
And essentially you have  I mean, for us, we're  creating private  clouds and being  able to work in a very  consistent manner with data  protection requirements  of all the publishers  we're dealing with.  But essentially, you  are hitting a wall.  It's giving you an answer.  It's not absorbing it into the  body of the actual model itself.  And then essentially  you're utilizing  the, the output of that.   
And I think also having  also reference into this,  you can run models entirely  privately outside of this  so that there's no slippage.  So, you know, you don't have any  kind of movement of materials  into the models themselves.  So for us, it's  working with publishers  and making sure that we are  absolutely consistent with all  the data policies,  which is what we're  doing at the moment with  publisher BI Publisher.   
Great I know we're  slightly over time.  I'm just going to fire  to each one of you.  If you have any comments  for this last question,  what AI tools are you  seeing scholarly publishers  trust and adopt  in-house to enhance  workflow and productivity?  What are some of the ways  these tools are being used?  So really, just what  are these AI tools?  Are people using  one way or the other  just quickly around  the virtual table?   
Well, I'll kick off.  I think what we're seeing  is AI tools not coming  in under the radar, but  becoming a standard part  of a lot of workflow offers.  So when we look at any  of the big providers  of editorial workflow solutions.  AI is becoming an  increasingly important part  of that for editing  purposes, for a whole range  of internal use cases.   
Thank you.  Anyone else?  Yeah I mean, I think we are  in the advent for of tools  in the editorial  and peer review.  I think that's still nascent.  And we are seeing those.  And people are  experimenting where  the areas where I've  seen the most adoption  has been around things  like research, integrity  and screening.   
So where there are  essentially technical tasks,  tasks that can be  taken over by AI tools,  and that's probably  where I've seen  the majority of the tools  being applied at the moment.  And, and structurally,  they're probably the easiest  to apply in that area.   Abby anything?  Yeah, I was just going to say  I'll give a quick plug for SSP  here.   
I sat in Dave's seat  a year and a half ago,  and we did an event  titled I efficiencies  to optimize workflow from  submission to publication.  Three case studies.  So that was actually  three different publishers  talking about how they're  using AI internally.  So if I'm assuming we're  all big SSP fans here.  So go ahead and log  into your account  and you should be  able to watch that.   
Sounds great Keith.  Anything last one.  If not.  Yeah no I leave it to  the experts on that one.  I'm following the legal  and policy issues.  You all are following  these other issues.  So it's really, really  interesting to hear it all.  So thank you.  Sounds great.   
Well with that Lori is got the  hook in me to end this thing.  So I'll turn it  back over to her.  Thank you.  And Thank you all.  I just wanted to Thank the  panelists, Keith, Kate, Richard  Navy, for your participation.  And back to you, Lori.  Yes on behalf of  SSP, Thank you all.  Thanks our attendees as well.   
Great great session.  Really wonderful information.  Encourage the attendees  to provide their feedback.  There's an evaluation.  You can scan this code as well.  For that, we'd like your input.  We're also planning for the our  in-person new directions event  in the fall.  And there's an AI  component in there.   
So if there are things  you would like to see  expanded on or added, please  put that into your evaluation  on suggested topics.  Don't forget, the SSP annual  meeting is may 28th through 30.  And early bird registration  is ends April 18,  so get your registrations  in as soon as possible.  Thanks again to our wonderful  sponsors, Silverchair  and access innovations.  And again, today's  webinar was recorded  and all registrants  will receive a link when  it's posted on the SSP website.   
And that concludes  our session today.  Thank you everyone.   Right now.