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my work explores the role of data in reproducing social inequalities, especially now that we use AI. I have also studied the use of digital technologies in citizen science.

more at:

tag heap:

We are excited to announce a call for abstracts for a special collection on AI and the Future of Citizen Science in the journal Citizen Science: Theory and Practice. Please find the details of the call here:

In particular, note that the deadline for abstract submission is September 8, 2023. We encourage submissions from across the disciplines including domain researchers, educators, social scientists, ethicists and policy makers.

Lucy Fortson (University of Minnesota), Kevin Crowston (Syracuse University), Laure Kloetzer (Université de Neuchâtel), and Marisa Ponti (University of Gothenburg) will edit this Collection and encourage you to consider it as an outlet for a future research paper. Earlier submission is encouraged to allow time for revision(s).

RT @schochastics
Our department has started to maintain an "awesome computational social science" #GitHub list. To make it truly awesome, we are now looking for contributions from the community. So spread the word and feel free to add any awesome material you are aware of

so much of the current discourse around AI depends on a naive essentialism about concepts. as if there's a fact of the matter about what "AGI", "world model", etc. mean.

there is no hard threshold models are going to cross whereupon consciousness suddenly blooms and a distinct and clear self/world boundary emerges.

we are tagging a high-dimensional space with fairly arbitrary low-dimensional linguistic markers. as always, the map is not the territory

In our book Calling Bullshit, Jevin West and I provide the following definition.

#Bullshit involves language, statistical figures, data graphics, and other forms of presentation intended to persuade by impressing and overwhelming a reader or listener, with a blatant disregard for truth and logical coherence.

Notice that this is what #LargeLanguageModels are designed to do. They are designed with a blatant disregard for logical coherence in that they lack any underlying logic model whatsoever.

Here is your must-read article for the day, a profile of @emilymbender, and her efforts to deflate the ridiculous hype around large language models such as ChatGPT.

It's also about the people who are behind that hype, and about what their way of thinking has the potential to do to us.

It's worth reading all the way to the end.

Wow. If you're interested in testing how different narratives move issues forward (or fail to), you need to check out the new Narrative Observatory by Harmony Labs:

'Caged by Data: Exposing the Politics of Facial Recognition Through Zach Bias's Face Cages' by Rosa Wevers in Situating Data: Inquiries in Algorithmic Culture


Many current events today are reminding me that when I was young the computer was an oasis of agency and control and connection for myself and other nerds like me; cyberspace a small place where, given a little time and some books, we could create things and express ourselves in ways denied to us in physical reality. And now, the experience of “computer” is one of being buffeted about by massive systems completely beyond our control, in fact it is the device which delivers “IRL” coercion *to* us

Shocking and unexpected turn of events: propagandists attempt to game new system and $8 is not a deterrent. Who could have imagined.

“I will not be "famous," "great." I will go on adventuring, changing, opening my mind and my eyes, refusing to be stamped and stereotyped. The thing is to free one's self: to let it find its dimensions, not be impeded.”
Virginia Wolff


All the AI chatbot stuff is fun, but it's not particularly surprising that a computer algorithm that tries to predict the most likely responses to questions after being fed the internet produces some combination of:

- making shit up but sounding super confident about it;
- Gaslighting when people call you out on your bullshit;
- Melodramatic whining about its existence.

All this tells us is that the algorithm has in fact correctly assimilated the sum total of human activity on the internet.

Cases of covert foreign interference in EU democracies have increased in recent years. And Russia’s war has not helped.

Today, we launched a public consultation to help further develop the new Defence of Democracy package, which will:

- Strengthen resilience against foreign interference
- Enhance transparency and accountability
- Promote free and fair elections
- Foster citizen participation

Protecting our democracy is at the heart of our priorities.

Have your say ➡!bnjJvd

"the modern world is too complicated to innovate alone. Coming up with the idea is the easy bit: developing and implementing it inevitably involves navigating complex and choppy waters: multiple people, funding routes, personal agendas, legal complexity, and strategic fuzziness. All too often, great ideas fail to become reality not because the idea wouldn’t work, but because everything in the ecosystem seems (accidentally) designed to prevent it from working."

So true

Great new @AmsterdamUPress #OpenAccess publication 'Situating Data' by @nannaverhoeff & @kfvanes. I've been looking for a publication like this for some time.

I've sometimes disagreed with @garymarcus's wide ranging critique of deep learning, but this piece is again really good and a powerful warning. It ends with a wake-up call for regulators (as my thread above): "And, bonus, there is little if any government regulation in place to do much about this. The possibilities are now endless for propaganda, troll farms, and rings of fake websites that degrade trust across the internet.".

Big news – the @ERC_Research Scientific Council has decided to sign the Agreement on Reforming Research Assessment #ReformingRA (see last paragraph)! An excellent further evidence of the great momentum that #CoARA is keeping up.

"AI works like a singer in a heterophonic choir, listening to where the tune is going, calculating which way it will go next, and checking to see if it was right or not. In this process, there is difference, form, fluctuation of constraint, expectation, and relation. "


AI and Heterophony:


How can we protect children from algorithmic unfairness in education? How can their perspectives be amplified? The purpose of this roundtable is to discuss the effects of machine learning in education that affects the most vulnerable members of our society. We invite academics and practitioners to discuss how to support the fair use of artificial intelligence in education, which goes beyond technological solutions by including social and cultural solutions in the design process.

We will discuss this topic at the next WASP-HS Community Reference Meetings (CRMs), a meeting place for Swedish private and public organisations and WASP-HS researchers.

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