Tereza Blazkova

@terezablazek.bsky.social

PhD Student in Social Data Science, University of Copenhagen AI & Society | Algorithmic Fairness | ML | Education Data Science https://tereza-blazkova.github.io/

⚠️ Despite all the hype, chatbots still make terrible doctors. Out today is the largest user study of language models for medical self-diagnosis. We found that chatbots provide inaccurate and inconsistent answers, and that people are better off using online searches or their own judgment.

Banner image with screenshot of scientific article from nature Medicine, as well as two panels from the study method and results

Join us for a Data Discussion on December 12! 📅 Yani Kartalis will discuss content plurality in Greece’s fragmented media system, while Tereza Blažková will present on participatory design in predictive modeling for student success. Event🔗: sodas.ku.dk/events/sodas...

SODAS Data Discussion 4 (Fall 2025)

SODAS is delighted to host Yani Kartalis and Tereza Blazkova for the Fall 2025 Data Discussion series!

sodas.ku.dk

Check out @isabelcorpus.bsky.social's fantastic thread on our paper studying the effects of a "write with AI" button on change.org! ✍️ Spoiler: the effects of AI aren't always positive.

Isabel Silva Corpus@isabelcorpus.bsky.social · 11mo ago

Excited to share a new working paper! What happened when Change.org integrated an AI writing tool into their platform? We provide causal evidence that petition text changed significantly while outcomes did not improve. 1/ arxiv.org/abs/2511.13949

Join us for a Data Discussion on Friday, November 7! 📅 Daniel Juhász Vigild will start by exploring how government use of AI impacts its trustworthiness, while Stephanie Brandl will examine whether LLMs can identify and classify fine-grained forms of populism. Event🔗: sodas.ku.dk/events/sodas...

SODAS Data Discussion 3 (Fall 2025)

SODAS is delighted to host Daniel Juhász Vigild and Stephanie Brandl for the Fall 2025 Data Discussion series!

sodas.ku.dk

While human behavior and the data describing it evolve over time, fairness is often evaluated at a single snapshot. Yet, as we show in our newly published paper, fairness is dynamic. We studied how fairness evolves in dropout prediction across enrollment and found that it shifts over time.

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