Ulrike Hahn

Academic @Birkbeck, Univ. of London
Centre for Cognition, Computation, and Modelling

was just at Netherlands Institute for Advanced Study working on Digital Democracy with Davide Grossi and Michael Maes

and then at the Munich Centre for Mathematical Philosophy, LMU

now back in London

works on #rationality #argumentation #testimony #SocialNetworks #misinformation #ComputationalSocialScience #DigitalDemocracy

Dept. Page: https://www.bbk.ac.uk/our-staff/profile/8009667/ulrike-hahn
Research Centre: cccm.bbk.ac.uk
SciBeh Initiative: SciBeh.org
was just at NIAS: https://nias.knaw.nl/themegroup/social-media-for-digital-democracy-theory-applications-algorithms/

Public Key

npub1u328aygluds5ft6n3ngrwjmd3ptq9j4xryeek708ryw524krrndqt4xcf5

NIP-05 Address

ulrikehahn_at_fediscience.org@momostr.pink

Profile Code

nprofile1qqswg4r7jy07xc2y4afce5phfdkcs4sze2npjvum08n3j8292mp3ekspz3mhxue69uhhyetvv9ujuerpd46hxtnfduqs6amnwvaz7tmwdaejumr0dssqxms3

Publishing to

relay.damus.io

nos.lol

Author Public Key

npub1u328aygluds5ft6n3ngrwjmd3ptq9j4xryeek708ryw524krrndqt4xcf5

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Published at

2026-04-13T17:16:04Z

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0 Metadata

Event JSON

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Last Notes

2026-03-20 14:11:49 UTC

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there will hopefully be restricted visibility posts in ATproto eventually. But they weren’t part of the original design

2026-03-20 14:08:50 UTC

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I think the legal aspect matters. And once someone downloads an ATproto post it’s not going to vanish by the author deleting it either. There is a freely available archive tool that lets you download your entire Bluesky post history with two clicks. And given how ATproto works, that means you can use the same tool to download, archive and search another users entire history of posts as well

2026-03-20 13:56:24 UTC

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there isn’t a permission less firehose, though?
it seems both legally different to me and different in design:
https://write.as/ulrikehahn/bridging-to-bluesky-the-open-social-web-consent-and-gdpr

2026-03-20 13:49:45 UTC

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reminder, the Bluesky team explicitly mentioned commercial use of their data in their 2024 arXiv preprint:
“…as Bluesky grows, there are likely to be multiple professionally-run indexers for various purposes. For example, a company that performs sentiment analysis on social media activity about brands could easily create a whole-network index that provides insights to their clients. Web search engines can incorporate Bluesky activity into their indexes,…” pg. 5
it’s been part of the model from the start…

2026-03-14 20:55:07 UTC

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I don’t think banning is likely or feasible

2026-03-11 14:03:51 UTC

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Is that what is happening here? I think these arguments stem from deep concern; unfortunately that isn’t enough to make them compelling. I genuinely wish it were as simple as “this seems bad, let’s not do this”, but unfortunately I think it really is not. As @SyliviaFysica said, the very fact that developments are so concerning makes it all the more important that we don’t fool ourselves. In that context, I don’t feel it’s the ethical issues that I’m struggling with, it’s that the introduction of LLMs seems both so transformational and so uncontrollable.

2026-03-11 09:00:55 UTC

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We really need ways to cope with generative AI and control it, but they are going to have to be able cope with its unique features.

2026-03-11 08:56:30 UTC

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OK, let’s destroy the LLMs themselves, the Luddites can be our inspiration!
never mind that they did not halt the advance of the mechanical loom, the only “thing” one needs about LLM for use is the weights. These too are infinitely copy-able.

2026-03-11 08:50:24 UTC

‘Technology is not inevitable. We can just not do LLMs.
Tobacco apparently is an illustrative example. Asbestos too.’
Cigarettes are still on sale. Tobacco companies still exist. Despite decades of monumental efforts. And the parties deeply invested in developing AI technology are not just large corporations but the most powerful state actors on the planet.
Asbestos is a distinct physical substance, that we can detect and remove. LLMs produce linguistic output which is not. Text is infinitely replicable and can seamlessly be transformed into another modality, speech. The linguistic output of LLMs is impossible to reliably distinguish from human output by its very nature.

2026-03-10 15:24:57 UTC

Judith Fan is the recipient of the 2026 Lila R. Gleitman Prize. Dr. Fan is an Assistant Professor of Psychology (and, by courtesy, Education & Computer Science) at Stanford University. She received her Ph.D. in Psychology at Princeton University in 2016, following an A.B. in Neurobiology and Statistics at Harvard College.
Congratulations!
https://cognitivesciencesociety.org/gleitman-prize/
#cogsci

2026-03-10 12:00:06 UTC

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sadly had same experience with Libreoffice including losing documents…

2026-03-09 12:18:06 UTC

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I’m also inclined to think that online social media has played a big part in this and that it was a consequential step into a negative feedback loop when the news started reporting on “what people are talking about on Twitter right now” - not only is virality on social media not epistemologically grounded in the right way, the growth of social media undermined the advertising model of legacy media, leading to shrinking resources for direct reporting, and ever greater journalistic reliance on what is being said, which in turn….

2026-03-09 12:06:38 UTC

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agreed: I think we fundamentally broke the link between “how impactful is this” and “how much noise does this make” over the last decades. So a formerly reasonable attention-based indicator (how much are people talking about this, responding to this?) has now lost much, if not most, of its diagnostic value….

2026-03-09 10:59:52 UTC

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the same goes for any strategy that rests largely on trying to dissuade people from using AI by telling them it doesn’t work and it’s evil, because that strategy’s effectiveness presupposes that people’s evaluations of the actual system capabilities, what they personally could or could not do with these systems, as well as what utilities/values are involved in using them will ultimately match your own.

2026-03-09 09:42:37 UTC

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indeed, and I think we should all be able to agree that that horror indicates the limits of “ridicule as praxis” as a strategy for countering the development of AI systems and their impact, however well-intentioned that strategy might have been

2026-03-09 08:09:52 UTC

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I wish I knew…

2026-03-09 08:09:32 UTC

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unfortunately, one of the things current AI systems can do extremely well is increase the scale of responding. That limits the efficacy of a strategy focussed primarily on dissuading people from using these systems, because comparatively few actors can have very large impact

2026-03-09 07:46:18 UTC

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the negative transformative effects that AI is currently having stem as much from the things these systems can do well as they do from the things that they can’t, and focussing exclusively on the “can’t” has, I think, been counter-productive to anticipating threats, countering them or adapting to them

2026-03-09 07:36:38 UTC

it’s unfortunate that so many of the voices concerned about the adoption of AI have been so singularly focussed on “hype” as the main problem and pushing back against “hype” as a core strategy, because that hasn’t helped us deal with the very real, and in many cases very destructive, transformative effects the technology has actually been having

2026-03-08 14:04:06 UTC

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literature
"If we're not prepared to deal with the fraud that's already occurring, then we're certainly not prepared to deal with what generative AI can do to scientific literature”

2026-03-07 08:28:42 UTC

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the only bit I’d take issue with in the article is the comment about the brokenness of search - I think that’s actually functionally connected to what has made the fediverse what it is (I’m aware that’s a controversial position)

2026-03-06 15:44:59 UTC

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thanks! hadn’t come across that! will read asap!!

2026-03-06 11:56:51 UTC

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but I also think that evidence that LLM output varies in surprising ways in response to changes in features that we would not consider to be substantively relevant to the “task” as we envision it goes way beyond the moral judgments, there’s so many examples, and they seem really pertinent to evaluating science
the difficulty with low probability outputs highlighted in the embers of autoregression paper seems pretty relevant to the science context, I think
https://www.pnas.org/doi/10.1073/pnas.2322420121

2026-03-06 11:42:34 UTC

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pretty much the only thing I’ve been saying for the last three years about LLMs is that claims about their performance and behaviour are empirical claims and we need to treat them as such ;-)

2026-03-06 09:48:24 UTC

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indeed

2026-03-06 09:47:25 UTC

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Relatedly, the most productive way to deal with baseless comments is to try and see what about the manuscript might have led them to go wrong….
not always, but quite often, there are fairly simple fixes that cut off entire paths to error. These can, in fact, make papers better, last but not least, because scientific papers are attempts to *communicate to a specific audience*
it is conceptually odd to have the evaluation of communicative success happen by anyone other than that target audience, and it feels like it would require lots of empirical work to show replacing a sample of the intended audience with some “other” (human or machine) didn’t lead to outputs that were ultimately less fit for purpose

2026-03-06 09:40:21 UTC

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THAT is an excellent point

2026-03-06 09:35:41 UTC

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that made me laugh on a gloomy morning… thank you!

2026-03-06 09:29:40 UTC

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indeed…
here’s the paper:
https://doi.org/10.1098/rsos.241229

2026-03-06 09:15:46 UTC

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the COI issue is an interesting one… given the current state of the literature on “deceptive” behaviour in LLMs should one allow an AI to review papers about AI performance from that perspective?

2026-03-06 08:55:45 UTC

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Tom, I wouldn’t class the repeated findings whereby LLMs give very different answers in response to accidental features of the input as compatible with “consistency” in the way it is implied here.
Just a few examples of many: the evaluation of moral judgments differing as a function of whether the two alternatives are labelled a) and b) or 1) and 2) (see Demberg et al), or the many examples in the Embers of Autoregression paper such as the accuracy of shift sequences depending on the size of the shift, or the accuracy of arithmetic operations depending on the actual numbers etc. etc.

2026-03-06 08:14:21 UTC

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there’s a fair bit of conjecture about the actual behaviour of LLMs as reviewers here, isn’t there?
the idea that AI will stay more relevant and be less biased than Reviewer 2 feels like it needs some empirical data…

2026-03-06 07:41:20 UTC

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the number of scientists has long been growing exponentially, and (rather unsurprisingly) so has the number of scientific publications, and now there are tools that allow each individual to become exponentially more ‘productive’…
I’m sure self-ranking will really help ….

2026-03-06 07:37:34 UTC

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that was exactly my response - everything we know about “impact” and the relative merits of funding foundational vs. applied research suggests to me that it is incredibly difficult, if not impossible to foresee how “useful” a piece of research will turn out to be….
(my favourite example is Boolean algebra, imagining Boole’s head of department and impact officers asking in 1847 “so what have you been working on, George? How exactly does that benefit society?”)

2026-03-06 07:05:14 UTC

another band-aid for coping with AI induced productivity increases….
https://www.nature.com/articles/d41586-026-00059-0

2026-03-06 06:38:46 UTC

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and my perception is that, contrary to what one would hope, the pre-print model looks set to collapse before commercial publishing does precisely because more information increases the need for more curation
https://write.as/ulrikehahn/is-ai-killing-scientific-reform

2026-03-06 06:32:58 UTC

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my question is more “what’s the feasible alternative” - at the core of the AI problem is the ability for (sheer endless) over-production relative to our capacity to process, evaluate, and integrate results. That structural problem doesn’t magically evaporate because you get rid of commercial publishers, or a particular unit of scientific record keeping and dissemination
given that science is a communal, inter-subjective, incremental endeavour it’s going to need *some kind of* records for information. And if the problem is “too much information”, how will the format alone solve it?

2026-03-06 06:20:31 UTC

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? and then what …
how do we keep hold of and share scientific results?
I don’t understand the idea that papers are just units of measurement for job applications, frankly

2026-03-05 17:22:45 UTC

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I see- I agree that the context matters and you might also definitely be able to `do activism' by presenting the evidence
but the core difference remains, to my mind, that not engaging with all of the evidence is 'bad science' while advocacy does not require that (rather, whether I do or do not, in communication or otherwise, is an instrumental decision: does it help or hinder..)

2026-03-05 14:36:38 UTC

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that is a helpful distinction and related to what I'm interested in here, but more in an implicit way, and I'm interested in capturing that implicit bit more clearly.
To put it differently, I could potentially be an expert on a topic but, say, publicly act as a lobbyist, so 'expertise' is relevant here by virtue of (genuine) expertise requiring a mode of engagement that engages with all of the evidence.
So expertise requires the particular mode of engagement that I think characterises science, but the fact that I'm an expert doesn't necessarily make all my communications in that mode.
And if what I'm trying to understand are lines like that between 'objective/subjective' or 'political/apolitical' I think that mode matters.
make sense?

2026-03-05 11:18:13 UTC

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this deserves a longer answer than I have time right now, but I think I'm not really in first instance thinking about the contrast between the scientist and the politician (ie the parlamentarian, or member of government) but between Me-the-scientist and Me-the-citizen.
As a citizen, I can freely advocate for actions/positions and there is no obligation (legal or otherwise) that in doing so I *have to* engage with other positions
at least, not in any straightforward way that I can see

2026-03-05 10:02:07 UTC

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I think they can blur, among other things, because I think there can be contexts where Ulrike-the-scientist can rightfully say "this is the best course of action" (as a declarative statement)
so the action vs. fact distinction is only sort of correlated with the roles....

2026-03-05 09:58:22 UTC

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yes, I agree that's one way in which the roles are often different, but I think there's more to the contrast than that-
I am, after all, free a a citizen to engage in political advocacy (that's part of free speech), so there is an issue about the line between me saying things as Ulrike-the-scientist and Ulrike-the-citizen.
and, I guess what I'm arguing is that looking just at statements themselves isn't enough to distinguish these two roles

2026-03-05 09:48:06 UTC

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one immediate consequence of this is that issues around the public pronouncements or stance taking of scientists will arise to the extent that it's not clear from statements themselves what mode of engagement underlies them

2026-03-05 09:14:45 UTC

random thought: discussions around the relationship between science and politics, science and values, or science and activism have never completely resolved into a satisfactory answer for me and I just wondered whether the real issue is that science and political action are associated with *different modes of engagement*.
Scientific integrity, to me, includes engaging in good faith with all of the evidence, that means also a demand to engage with arguments by 'the other side'. Politics or activism does not require me to do that in any shape or form (though some such engagement might sometimes be instrumentally useful). That's the actual tension...
all of the discourse between 'objective' and 'subjective' or 'apolitical politics versus everything is political' is stuck on not quite the right thing
make sense? all comments welcome...

2026-03-05 07:54:33 UTC

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the prevalence (and the range of use cases) certainly took me by surprise ….

2026-03-05 07:15:35 UTC

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last May, I asked the room of 60 in my lecture how many of them had used ChatGPT (or similar): every single person raised their hand

2026-03-05 07:05:01 UTC

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this is a fantastic visualisation!

2026-03-05 06:56:05 UTC

“The exception was reality distortion: users who adopted false beliefs and appeared to act on them continued to rate their conversations favorably.”
https://www.anthropic.com/research/disempowerment-patterns

2026-03-04 16:17:50 UTC

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that’sa fair point, but there is still a bit of a difference in that those techniques weren’t generative. So their applicability was much more restricted - (which in turn affords easier regulatory control)

2026-03-04 07:38:40 UTC

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one of the things that seems characteristic of current AI systems to me is that their profile respective strengths and weaknesses make them disproportionally useful for bad actors: the limits on reliability are a stronger bar to positive contributions than they are to destructive ones, where the ability to simply scale responses dominates

2026-03-04 05:39:33 UTC

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it’s a great book, I agree!

2026-03-03 14:02:32 UTC

the problem with large language models is not that they are merely stochastic parrots, it’s that they are not merely stochastic parrots

2026-03-03 08:03:18 UTC

horrifying….
https://www.theguardian.com/technology/2026/mar/03/iran-war-heralds-era-of-ai-powered-bombing-quicker-than-speed-of-thought

2026-03-03 06:42:07 UTC

RE: https://wisskomm.social/@rwthaachenuniversity/116124828842725742
another German university joins Mastodon. Say hello!
#note16dn…vzws

2026-03-02 16:56:49 UTC

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there’s still an issue with the distribution of expertise, though, no?

2026-03-02 08:43:16 UTC

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same here

2026-03-02 08:32:15 UTC

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thanks! any particular aspects?

2026-03-02 07:58:28 UTC

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I haven’t evaluated its performance but Apertus seems to be trying https://www.swiss-ai.org/apertus

2026-03-01 21:09:33 UTC

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“Our contributions. We demonstrate that LLMs funda-
mentally change the picture, enabling fully automated deanonymization attacks that operate on unstructured text
at scale. We show this by phrasing deanonymization as a matching problem and showing LLMs can perform all steps needed to match accounts: extract identity-relevant signals from arbitrary text, efficiently search over millions of candidate profiles, and reason about whether two accounts belong to the same person. We show that the practical obscurity that has long protected pseudonymous users (the assumption that deanonymization, while theoretically possible, is too costly to execute broadly) no longer holds”

2026-03-01 21:06:42 UTC

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“Compared to classical deanonymization work (e.g., on the Netflix prize) that
required structured data , our approach works directly on raw user content across arbitrary platforms”

2026-03-01 17:37:47 UTC

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well, I can’t see meaningful outcomes without actually trying to understand what exactly people are using these systems for and how they think about the various implications (scientific and ethical) and engaging with that

2026-03-01 16:25:22 UTC

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who is “them” referring to here?

2026-03-01 13:10:10 UTC

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… it feels to me like where we’re headed is that all of this is becoming so easy anyone is at risk, and that risk is determined simply by the probability that someone holds a sufficient grudge…

2026-03-01 13:03:21 UTC

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so, you would like to change people’s minds then?

2026-03-01 11:44:50 UTC

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yes. It’s deeply worrying!

2026-03-01 11:28:19 UTC

the end of online anonymity?
https://arxiv.org/abs/2602.16800

2026-03-01 10:32:11 UTC

I personally use AI only in the context of research *on* AI. I intentionally don’t use it to facilitate my research and I currently feel that’s the right choice for me.
But I know many researchers choosing differently who are both excellent scientists and individuals with integrity who I respect as people.
The discourse which, like clockwork, either tells such researchers that they don’t understand how these systems work and they’re garbage and/or that the researchers themselves are morally deficient people isn’t changing minds.
It’s hard for me to see it as helpful.

2026-03-01 10:02:26 UTC

I note that posts here have seemlessly transitioned from “auto-complete system whose output is meaningless” to “if you use OpenAI or Anthropic you are abetting war crimes”
as someone who remembers the response here to Geoffrey Hinton expressing deep concern about military applications in 2023 I’m a little angry at the way public discourse about AI has failed us in the last three years

2026-03-01 09:55:01 UTC

as AI and scientific productivity is proving a contentious theme again this morning….
https://write.as/ulrikehahn/is-ai-killing-scientific-reform

2026-03-01 09:51:20 UTC

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I will leave that to you to puzzle out and now stop bombarding Ruth’s thread….

2026-03-01 09:44:27 UTC

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maybe that should be a clue that you are somehow missing the intended point?

2026-03-01 09:34:17 UTC

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Benjamin, maybe just reread the previous post of yours and ask yourself “what in this post am I saying that could possibly be new to the person I am addressing?”…and then see where that leads you

2026-03-01 09:16:23 UTC

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sometimes replies here leave me speechless…

2026-03-01 09:02:13 UTC

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I take “productivity” to refer to the efficiency of production of a good or service
readily available AI systems now can (and do) produce essay answers that I would have to assign a passing grade (and actually an increasingly good grade) given our marking criteria, and it can do that in seconds. It’s a huge problem for higher education.
How is that not a “productivity gain”?
I find the conflation of questions about what these systems can actually do (an empirical question!) with questions of desirability deeply counter-productive.

2026-03-01 08:30:28 UTC

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there are massive “productivity gains” everywhere:
- higher education is demonstrably being undermined by the fact that course work can now be (and is being) completed by AI query
- science is demonstrably buckling under a deluge of submissions
- democracy is demonstrably being harmed by AI based astroturfing
all of these are well-documented. “Controlled experiments” are not the only form of evidence ….

2026-03-01 08:04:11 UTC

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and if these systems were “just hype” we wouldn’t be seeing the widespread systemic threats and transformations we are now in the midst of - the hype discourse did nothing to prepare us for the damage and did us a disservice if you ask me

2026-03-01 07:58:09 UTC

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are you aware of this project: https://www.swiss-ai.org/apertus

2026-02-28 16:41:10 UTC

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ah, I see what you mean: I think of that not as a matter of external/internal but as the seeming need for a “global signal” to guide global adjustments…

2026-02-28 16:05:57 UTC

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I’m saying that any kind of NN whose target task is predicting what happens next (for example, next token prediction in the context of LLM training, or a recurrent network a la Elman, or the Bayesian brain hypothesis) elides the difference between an “external guide” and self-driven adjustment.
the whole point about those tasks, conceptually, is that they provide teaching without a teacher….

2026-02-28 15:54:33 UTC

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interesting thoughts, Nate! Personally, I find the points about the complexity of neurons more relevant in this context than the contrast to “being tuned by some external actor” given that a prediction task like next token prediction etc. feels like it elides that difference between the internal and the external…

2026-02-28 08:19:00 UTC

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this was completely predictable the moment public genAI tools were released and instead of trying to find ways to mitigate this threat we spent three years locked in a fruitless ritualistic debate between ‘over-hyped auto complete’ and ‘superhuman existential risk’

2026-02-27 05:41:47 UTC

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😢

2026-02-25 17:24:52 UTC

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interesting! I guess for me personally it really depends on what one’s specific question is: for me, those questions have always been about understanding *behaviour*. It’s then not a big step to take a connectionist model and say something like “well this particular error minimisation technique isn’t biologically plausible, but the main explanatory work is being done by the fact of some kind of error minimisation process over this kind of architecture…” so you can kind of conceptually bracket that (as a kind of note to self: one day fill in more biologically plausible component)… whereas if your primary explanatory target is the brain itself then that same mismatch is a much more fundamental problem

2026-02-25 07:23:23 UTC

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👆in German, sadly, but link to original paper in Cell Biology is in the piece: newly discovered ant species with different social structure
https://www.cell.com/current-biology/fulltext/S0960-9822(25)01621-5

2026-02-24 14:35:10 UTC

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let me know if it changes your mind….

2026-02-24 14:21:58 UTC

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have a look at this one too: Rafael Bogacz is most definitely interested in understanding brains, not AI …
https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(19)30012-9

2026-02-24 14:18:32 UTC

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well, I would assume that everyone who was an author on the original PDP books (which includes Hinton, but also Jay McClelland) would have said they *were* trying to understand the brain “on it’s own terms”….😉

2026-02-24 13:34:26 UTC

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there are references on this topic of the neural plausibility/implausibility of backpropagation going back every decade over the last 40 years since the publication of the PDP books.
Here’s a more recent example from 2020 https://www.nature.com/articles/s41583-020-0277-3

2026-02-24 13:27:51 UTC

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the discussion about neural networks, back prop, and brains, has been rumbling on in cognitive science since the late 1980’s…so I’m going to take issue with your use of “everyone” here, Nate ;-)

2026-02-22 13:48:29 UTC

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and that would be a net loss for everyone….

2026-02-22 13:34:17 UTC

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I love it here, but there is more content relevant to my research on LinkedIn (psych, cogsci, computational social science), and, from what I see there’s more in those areas than on Bluesky as well…

2026-02-22 11:50:50 UTC

SocarXiv struggling to formulate an AI use policy https://socopen.org/2026/02/22/where-should-socarxiv-draw-the-ai-line/

2026-02-22 11:43:47 UTC

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it pains me to say it, but in terms of academic discourse, LinkedIn is ahead of anything else right now….

2026-02-19 11:35:18 UTC

This is an amazing book. I came across it only because @npub1sac…kcpe mentioned it in passing a while back and it took me some time to get through, but I think it’s among the most clarifying books I’ve ever read. Anyone with even a passing interest in online communication should read it:
Joshua Meyrowitz (1985) No sense of place: The impact of electronic media on social behaviour. OUP
https://fediscience.org/system/media_attachments/files/116/097/071/194/889/250/original/a5fcc9e839d821a9.jpeg

2026-02-19 07:07:17 UTC

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what I still don’t see is how resources, even with rearranging, will keep up with ever greater production…

2026-02-18 07:05:12 UTC

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really useful paper!

2026-02-13 15:30:22 UTC

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I’m more worried about the near term degradation of our information environment, but I see what you mean…

2026-02-13 13:02:49 UTC

dismissing any consideration of the risks current AI systems pose in their current form as “AI boosterism” seems deeply unhelpful to me….

2026-02-13 10:32:42 UTC

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we are not ready…..