Organized by InstituteQ and Kimmo Tuominen

Some of the thinking behind those quotes can be found here, and a recently accepted research article (in English) here.

Here’s the abstract:

Has the emerging field of computational social science grown increasingly intellectually integrated over time, or does the field remain fragmented into several distinct epistemic communities? Because research literature is still inconclusive in this regard, we use data about citations to study the development of the intellectual structure of computational social science. The findings suggest that network science has maintained a central position in computational social science over the years. Contrary to common perceptions, however, the field has not become more integrated over time. Rather, the network of cocitations among the field’s key authors exhibits increasing clustering and greater average path length over 2000–2020, indicating that computational social science is fracturing into various subcommunities, with loose connections to each other.

Keywords: cocitation analysis, cohesion, computational social science, intellectual integration, interdisciplinarity

Here’s how I got into diversity research. I remember the moment quite vividly: in November 2013 I was in my kitchen watching a Coursera video by Scott Page, an economics professor at Michigan University.

In that video, Page was presenting his (and his coauthor’s) famous finding from 2004, the diversity trumps ability theorem. The upshot of the theorem is that when people solve problems together, in groups, it makes sense to maximize the diversity of the group members instead of building the group from the individually best performers. This finding has later had lots of implications e.g. for hiring, when companies started following it in recruiting (McKinsey 2015). (But for some of the rather depressing recent developments, see here)

I thought that was really interesting, and started thinking about what such results would mean in my own research field, philosophy of science.

Ever since Thomas Kuhn’s Structure of Scientific Revolutions in the 1960s, philosophy had had a discussion about the role of rationality in science. Paul Feyerabend had suggested a very radical diversity thesis: “anything goes”, i.e. there is no scientific method, and we should let all flowers bloom. No theory should be permanently discarded, because later on, it may turn out the be true, after all.

How diversity works in research

Others were more cautious. For example in the 1990s, Philip Kitcher argued that no departures from rationality were needed, but instead, a beneficial division of labor between scientists would be enough to get to good collective outcomes.

Using modeling techniques from biology and economics, Kitcher’s paper launched a new field, which could be called social epistemology of science, and that’s the field that I’ve been working in since that afternoon in 2013 (O’Connor 2023, Šešelja 2023).

Here’s the agenda of the field (in the words of Steve Fuller, 1988):

“How should the pursuit of knowledge be organized, given that (…) knowledge is pursued by many human beings, each working on a more or less well-defined body of knowledge and each equipped with roughly the same imperfect cognitive capacities, albeit with varying degrees of access to one another’s activities?”

But how is this related to diversity? I should clarify some of the central concepts I’m talking about.

My main interest has been in cognitive diversity: We say that a group is cognitively diverse when its members differ, for example, with respect to their disciplinary background, expertise and skills, problem-solving heuristics, or strategy that each group member uses to explore a common field of research. 

Cognitive diversity can be distinguished from demographic diversity (its members occupying different social locations, e.g., with respect to gender, class, ethnic identity, nationality, and race), and from social value diversity (when its members endorse different social and political values, or act as representatives of different interest groups). More on these concepts in Rolin et al. 2023.

All these kinds of diversity can be understood as being intrinsically valuable, but what both Page and Kitcher are trying to articulate is the indirect benefit to science of having more diversity in the workforce.

(In political philosophy, a similar case has recently been made for democratic decision making, most forcefully by Helene Landemore.)

Cognitive diversity was of the key concepts of my 2020-2025 Academy fellow project called Modeling the republic of science. Obviously there is no one way that diversity works in science. You could say it has several functions, and these functions are served by different mechanisms.

The table is far from exhaustive, and does not include the potential downsides or costs of diversity to research. Here I just want to point out one thing (Row 5): Philosophers don’t agree about most things, but in philosophy of science there’s almost a universal consensus that scientific knowledge cannot be found in the brain of any particular scientist. Instead, scientific knowledge is collectively held by the scientific community. Furthermore as feminist philosopher of science Helen Longino (1990) famously argued, the objectivity of that knowledge is guaranteed by critical exchange of arguments among a diverse community of scientists.

Cognitively diverse university

In 2023 my colleague Säde Hormio and I took some of these ideas and applied them to higher-education research. Based on diversity arguments, we put forward a new argument for academic freedom: Namely, that the capacity of universities to serve the society by discovering significant truths depends on us having sufficiently diverse repertoires of (1) problems we study , (2) solutions we develop, and (3) people thinking about the world in different ways.

Alien intelligences and existential implications

Here’s what I’m doing now (2026-05). I have a new research project starting on June 1st, 2026: Scientist in the loop - Automation of scientific discovery (SCI-AI) is funded by an ERC Consolidator grant. The main aim of the project is to better understand how AI is changing scientific discovery.

In terms of methods and theory, SCI-AI continues what I did before in my study of collective problem solving in science. But now the question, from the diversity viewpoint, is what is happening to science now when these alien intelligences are entering academic research.

Not everyone likes that framing, and I understand why. I myself have mixed feelings about the AI transition. But I think it’s a real thing, the models are quite capable, and the benefits to advancement science can be really big. That said, risks and downsides are just as real.

I recently read this Terence Tao interview, and I agree with him that AI will change research in almost all scientific fields. But I feel strong nostalgia for pre-AI times. Many of the research skills that I spent decades learning are quickly losing value. If I could turn back the clock, I’d be perfectly happy doing my research in those pre-AI times. I’m one of those, mentioned by Tao, going through the five stages of grief.

SCI-AI hasn’t really started yet, but one thing I want to mention is this idea that AI can be a useful reminder to us humans of our own limitations. In this paper that I wrote together with Renne Pesonen from Tampere University, we argue that based on scientific findings (and conceptualizations) alone, there are no in-principle reasons why machines could not be intelligent, creative, or genuinely understand language.

Most of us have intuitions to the contrary, and I’m sure that the view we outline is not a popular one. But we hope it’s a discussion opener. I think that from the diversity point of view, AIs, these alien intelligences, provide us humans an opportunity to look in the mirror, and understand our own humanity more clearly. We’ve actually had superhuman cognitive capacities around for a long time (e.g. pocket calculators with their amazing capacity to multiply large numbers), but the fact that LLMs cracked at least the syntax problem for natural language really rubs the point in our face. We argue that whether impressive feats of LLMs mean that they are “really” intelligent or not isn’t all that relevant. What matters more, from an existential point of view, is that they offer us humans an opportunity to develop some epistemic humility, that is, the capacity to see both our intellectual strengths and weaknesses more clearly.

Thinking meat, crazy, right? (Terry Bisson 1991, worth checking out …)

(Cross-posted to leaflet.pub)

These policies outsource researcher evaluation to extra-academic agents, and result in absurd demands on academic review panels. If implemented, they can lead to significant harm to the quality of Finnish research and self-regulation of the academic community.

Here’s an AI summary (light edits):

Kuorikoski and Reijula argue that recent efforts to increase objectivity in academic hiring —- though well-intentioned – may actually threaten the quality and autonomy of science. They critique developments in Finland such as the Research Council of Finland’s guidelines on responsible evaluation and the University of Helsinki’s partial outsourcing of doctoral school admissions, which emphasize standardized and administratively controlled processes.

The authors introduce the concept of “HR-objectivity”, referring to a managerial model of objectivity that seeks to eliminate bias through formal, measurable criteria and external oversight. They contrast this with the epistemic objectivity of science, which relies on expert judgment, peer consensus, and tacit knowledge within research communities.

Kuorikoski and Reijula caution that treating academic evaluation like a typical HR process neglects the complexities of scientific quality and risks marginalizing expert intuition and contextual understanding. They emphasize that while fairness and transparency are important, over-reliance on procedural formalism can lead to superficial assessments and reduced academic freedom.

Ultimately, they argue that a misguided view of objectivity in hiring undermines science itself by eroding the community-based mechanisms that safeguard research excellence.

Here’s the link to the interview.

“If I had to pick one hypothesis to be tested in a real-world setting, I’d go with my work on bias and laziness” - says Matteo. Better find out what he’s talking about!

A manuscript version here

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Perintövero on yhteiskunnallinen arvovalinta

Ehdotus perintöveron poistamisesta on herättänyt paljon keskustelua. Puheenvuoroissa on korostunut taloustieteellinen näkökulma. Sen sijaan oikeudenmukaisuuteen liittyviä kysymyksiä ei juurikaan ole nostettu esiin.

Perintövero voi tuntua epäreilulta kun se osuu omalle kohdalle. Veroa voisi sujuvoittaa monin tavoin, mutta sen korvaaminen luovutusvoittoverolla olisi askel kohti epätasa-arvoisempaa Suomea. Ruotsissa muutos on johtanut sosiaalisen liikkuvuuden heikentymiseen.

Perintövero on tärkeä yhteiskunnan rakenteen kannalta, sillä se jarruttaa eriarvoisuuden kasvua. Sillä tasataan varallisuuden ja vallan kertymistä harvojen käsiin ja autetaan takaamaan mahdollisimman tasa-arvoiset mahdollisuudet kaikille perhetaustasta riippumatta. Hiljattain The Economist -lehti myös varoitti, että perityn varallisuuden kasautuminen voi murentaa yrittäjyyden, innovaatioiden ja talouskasvun edellytyksiä.

Verotus on aina arvovalintoja. Mihin suuntaan haluamme viedä Suomea?

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Julkishallinto pois pikaviestipalvelu X:stä

Miljardööri Elon Musk päättää X-pikaviestipalvelun tiedonvälitysperiaatteista. Valitsemalla X:n tiedotuskanavakseen julkishallinnon toimijat osoittavat hyväksyvänsä Muskin näkemyksen tiedonvälityksestä.

2010-luvulla sosiaalisen median pikaviestipalvelut avasivat uuden suoran kanavan politiikan, tutkijoiden, eri alojen asiantuntijoiden ja kansalaisten välille. Varjopuolistaan huolimatta sosiaalinen media on edelleen tärkeä suoran ja demokraattisen vuorovaikutuksen ja oppimisen väline. X on käyttäjämäärältään suurin pikaviestipalvelu. Musk osti Twitterin lokakuussa 2022 ja on sen jälkeen voimakkaasti muokannut alustan toimintaperiaatteita.

Julkishallinnon toimijoiden on aika poistua X:stä. Monissa tapauk­sissa palvelua tarjoavan yrityksen omistajan poliittiset näkemykset eivät ole ongelma sen käyttäjien kannalta, mutta X:n tapauksessa näyttää siltä, että Muskin poliittiset näkemykset vaikuttavat siihen, millaista sisältöä alustan algoritmi nostaa esiin. X ei ole neutraali viestintäkanava. Tästä seuraa että julkishallinnon X:n käyttö on poliittinen teko, eikä välttämättä linjassa arvojemme kanssa.

X:lle on olemassa hyviä vaihtoehtoja. Toisiinsa linkittyneet niin kutsutun fediversumin protokollat ja palvelut (muun muassa Mastodon, Bluesky ja jossain määrin Metan Threads) tarjoavat vapaan maailman vaihtoehdon, jossa käyttäjä voi omistaa omat sisältönsä eikä ole yhdenkään tietyn palveluntarjoajan armoilla. Fediversumin hajautettu rakenne tarjoaa julkishallinnon viestintään erinomaisen alustan. 

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In our view, the striking thing about LLMs is that they are an existence proof of structurally simple (but large!) systems that can perform many of the tasks we think require intelligence. The fact that they might do those things in a “wrong”, non-human, way is beside the point: Something we thought could only be brought about by the (so far) unexplained powers of the human mind is now done by a shallow system. This should give us pause, some epistemic humility and antidote to human cognitive exceptionalism.

Preprint available here and here

Here’s the abstract:

Can large language models be considered intelligent? Arguments against this proposition often assume that genuine intelligence cannot exist without consciousness, understanding, or creative thinking. We discuss each of these roadblocks to machine intelligence and conclude that, in light of findings and conceptualizations in scientific research on these topics, none of them rule out the possibility of viewing current AI systems based on large language models as intelligent. We argue that consciousness is not relevant for AI, while creativity and understanding should be considered functional traits that, in principle, can be implemented in a machine. Many arguments in circulation that claim current AI systems necessarily lack these traits either rely on human exceptionalism or mistaken understanding of human intelligence. Arguments that highlight alleged flaws in current systems—such as a lack of reliability, agency, or understanding—may be important, but they often obscure the many qualitative similarities between human cognition and current AI models. We further suggest that a critical examination of high-performance AI systems can serve as a mirror through which we can reflect on our own intelligence. Many arguments against the prospects of AI may prove disconcerting for naively optimistic assessments of capacities of the human mind.