New paper in Nature Human Behaviour.
I use a conjoint experiment to evaluate the capabilities of the latest models for context-sensitive moderation and compare the results with those from human subjects, demonstrating how social science techniques can enhance AI auditing. π»π€π¬
π’ Call for Reviewers: WOAH @ #EMNLP2026
WOAH is looking for reviewers to help evaluate submissions for our 10th edition at EMNLP 2026.
Interested in serving as a reviewer?
π docs.google.com/forms/d/e/1Fβ¦
Thank you for helping us make #WOAH2026 a success!
#NLP#AI#OnlineSafety
β¨New paper out @SpringerNatureβ¨ For 8 weeks around the 2024 US election, we randomly assigned 2,000 people to use social media algos we custom-built. Do engagement-based algorithms amplify intergroup, moral & emotional content does that distort how we see political norms? π§΅
Mercor has some interesting opportunities for getting involved in evaluating frontier AI models. I have found it an insightful experience, and you can get paid for helping to make AI less sloppy! Happy to chat if anyone has questions.
t.mercor.com/y6A4l
AI-generated summaries of history led to more liberal opinions compared to Wikipedia, while summaries by chatbots prompted to use a conservative framing produced more conservative opinionsβbut primarily among conservative readers. In PNAS Nexus: ow.ly/oYQH50YpOHO
ALT General strike participants leaving shipyards, Seattle, February 1919. This image of workers was taken at the Skinner & Eddy Corporation shipyard located between Dearborn Street and Connecticut Street (now Royal Brougham Way). The nitrate photo shows signs of deterioration on some light parts of the image.
Want to learn about computational social science *for free* and identify new research partners across academic fields? Apply to one of the 2026 Summer Institutes in Computational Social Science (described in yellow in the attached map) here: sicss.io/locations
New paper in Nature Human Behaviour.
I use a conjoint experiment to evaluate the capabilities of the latest models for context-sensitive moderation and compare the results with those from human subjects, demonstrating how social science techniques can enhance AI auditing. π»π€π¬
Article by @thomasrdavidson examines how multimodal LLMs evaluate hate speech: larger models aligned with human judgment in context-sensitive decisions, but pervasive demographic and lexical biases remain, and visual identity cues may amplify disparities.
nature.com/articles/s41562-0β¦
New paper in Nature Human Behaviour.
I use a conjoint experiment to evaluate the capabilities of the latest models for context-sensitive moderation and compare the results with those from human subjects, demonstrating how social science techniques can enhance AI auditing. π»π€π¬
Overall, these results show that MLLMs can make more context-sensitive moderation decisions than text-based classifiers. These systems still make mistakes, and context can cut both ways, eliminating some biases while enabling others. Human oversight thus remains essential.
I'm recruiting multiple PhD students for Fall 2026 in Computer Science at @JHUCompSci π
Apply to work on AI for social sciences/human behavior, social NLP, and LLMs for real-world applied domains you're passionate about!
Learn more kristinagligoric.com/ & help spread the word!
Should WOAH start a mentorship programme? π€
As the workshop grows, reviewer expectations are rising.
We donβt want contributors from adjacent communities penalised by *CL norms.
Senior PhDs and beyond could be mentors.
Share your thoughts:
π forms.gle/safif3rU2rs5S6H88
New pre-print on large reasoning models π¨π€π§
To what extent does LRM behavior resemble human reasoning processes?
I find that LRM reasoning effort predicts human decision time on pairwise comparison tasks, and that both humans and LRMs use more time/effort on harder tasks
Analysis of the reasoning traces for Gemini 2.5 shows that the model emphasizes second-order factors when faced with such decisions, helping to avoid common false positives like flagging reclaimed slurs as hate speech.
There are, of course, caveats: LRMs do not replicate human cognition, there are limits to their capabilities, and reasoning is not always faithful
Check out the preprint and feel free to share any feedback: arxiv.org/pdf/2508.20262