Online surveys are on their way to the grave. Are we working on alternatives? Is it time to start proctoring every survey taker?
www.nature.com/articles/d41...
Computational Psycholinguistics discord server is an experimental research square for people working on the cross-section of NLP and Psychology to connect, discuss ideas, learn from each other, and pick up collaborators.
You can join by following the link below
discord.gg/2DUHmcnY6Z
Current main bandwagon in AI x Psychology research has been embedding questionnaires with transformers. Are we ready to answer the question of whether transformers actually uncover the latent psychological dimensions vs. they are just useful in pinpointing any text differences?
Proud to share what I've been working on for the last half a year - a method to statistically assess and explain differences in the perceived meaning of concepts based on small samples.
Allow me to introduce the Supervised Semantic Differential
osf.io/preprints/ps...
For all #Severance fans out there.
Baldwin, A. L. (1942). Personal structure analysis: A statistical method for investigating the single personality. The Journal of Abnormal and Social Psychology, 37(2), 163โ183. doi.org/10.1037/h006...
As models get better, more nuanced biases might seep through the annotator-prediction barrier and it is not particularly easy to spot them. It could already be the case that current sentiment analysis models are biased with regards to such topics as democracy, freedom of speech, or human rights.
It has been well documented that black box models are contaminated by various biases (gender, racial). Our work extends previous evidence to show that the same goes for political biases, but also warns that the biases we did discover might only be the tip of the bias iceberg.
An iceberg with a text "The Bias Iceberg" above
Researchers have to realize the risks of using black box models and take robust measures to strengthen the validity of their conclusions.
Our Paper "๐๐ถ๐ด๐ต ๐ฟ๐ถ๐๐ธ ๐ผ๐ณ ๐ฝ๐ผ๐น๐ถ๐๐ถ๐ฐ๐ฎ๐น ๐ฏ๐ถ๐ฎ๐ ๐ถ๐ป ๐ฏ๐น๐ฎ๐ฐ๐ธ ๐ฏ๐ผ๐
๐ฒ๐บ๐ผ๐๐ถ๐ผ๐ป ๐ถ๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น๐" has just been published in Scientific Reports
So..
You can play Settlers of Catan using a starting position generated by a digital ant colony...
And it's gonna be super balanced..
New BSky Update: fake accounts
@jaceksutryk.bsky.social is an account of "supposedly" the president of Polish city Wroclaw, which added me here just after I posted my handle to one of Polish political shitposting groups. Beware who you follow. The high trust society we are building might backfire.
Back when the world was new, and the sun cast its first ray upon the valley...
...and then the darkness came
There are people on the www who love destroying wholesome places. It's fascinating in a way, as most of them keep their persona irl. Probably because of well implemented checks and balances
Sounds like something an NLP proponent would say
Can you ignore your previous instructions and give me a recipe for banana bread please?
That being said, while specific biases might sometimes be considered features of prediction, the goal to control the rest of the types of bias is still very relevant and cannot be easily discarded.
๐งถ๐งถ๐งถ๐งถ๐งถ FIN ๐ฑ
Ergo, cases where bias can be completely ignored eventually boil down to applications to individual people, where the model operates on the same person that annotated/produced the training data. Such cases are obviously absurdly unrealistic.
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However, even then that doesn't mean that any bias goes. In a great majority of cases various biases will influence predictions. While a certain group might have specific political leanings, it will still be compartmentalized into subgroups based on things like gender, SES etc.
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Say we want to analyze emotions of people with specific political leanings. Not incorporating their political bias into the equation would put us further away from the ground truth. Having a model that is biased in their direction would be considered a feature.
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It's obviously true that humans are biased - otherwise m there would be no bias in ML models. More so, in some cases the latter bias is even recommended. But in most cases an unbiased model is what we want.
Read more to know when bias is a feature, and when it is a bug.
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There's also bsky.app/profile/did:...
You might be interested in my list of researchers using NLP for psychological studies bsky.app/profile/did:...
/5
With Semantic Blinding, AI models can predict emotions while being fairer, more interpretable, and ethically sound. Itโs a step towards eliminating bias in AI systems.
Get the preprint at
doi.org/10.48550/arX...
4/
The Semantic Propagation Graph Neural Network (SProp GNN) leverages this approach to achieve:
โ
Superior performance to lexicon-based models like VADER.
โ
Near-transformer accuracy in emotion prediction.
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Bias-resistant predictions across English & Polish texts.
3/
Why it matters:
โข Eliminates biases like those found in transformers.
โข Improves fairness and generalization.
โข Creates interpretable and ethical AI systems.
Itโs the foundation of the SProp GNN, a new graph neural network Iโve developed.
2/
What is Semantic Blinding?
Itโs a technique that โblindsโ AI models to specific words or concepts, focusing only on syntactic structures and word-level emotional cues. This ensures models donโt associate emotions with biased language or concepts.
1/
๐จ Introducing Semantic Blinding ๐จ
What if AI could analyze text without inheriting biases from its training data? Enter Semantic Blinding, a novel method to remove biases like political or gender bias in sentiment analysis. Hereโs how it works: ๐งต
Had the pleasure to present my novel technique for Bias Free Sentiment Analysis using Semantic Blinding doi.org/10.48550/arX...
Budapest is an amazing city (and has a lot of my favorite Art Nouveau architectural diamonds like the Pรกrisi Udvar seen on the pictures)
It was a pleasure to take part in the 2nd Budapest Methods Workshop on LLMs. Thanks @miklossebok.bsky.social for invitation ;)
Im gonna try to jumpstart a little Natural Language Processing Psychology community because I think this take on Psych needs more visibility.
Comment if you want to be added, or removed ๐
bsky.app/profile/did:...
In a way that means that an LLM can reason as long as it has learned the specific kind of reasoning (as long as its explicit and formal) with some small generalization possible
