Who's Who of AI

Debora Nozza

79 trust researcher @deboranozza.bsky.social · 451 followers
Assistant Professor at Bocconi University in MilaNLP group • Working in #NLP, #CSS and #Ethics • She/her • #ERCStG PERSONAE
What they're sharing

Articles & links

↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

For today's reading group, @marlutz.bsky.social presented "State media control influences large language models" by Waight et al. (2026) Paper: www.nature.com/articles/s41... #NLProc

State media control influences large language models | Nature nature.com
AI Weekly's analysis →
  • Chinese state-media content appears in typical LLM training sets at roughly 41 times the rate of Chinese-language Wikipedia.
  • Across 37 countries, models prompted in the local language produce more regime-favorable responses in countries with lower press freedom.
  • A pretraining experiment with just 6,400 state-scripted documents pushed an open-weight model to pro-government responses nearly 80 percent of the time.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

For today's reading group, @esradonmez.bsky.social presented "RLHF May Not Reflect Genuine Preferences" by Ghafouri et al. (2026). Interesting thoughts on whether annotations are actually real preferences! Paper: arxiv.org/abs/2604.03238 #NLProc #RLHF

RLHF May Not Reflect Genuine Preferences arxiv.org
AI Weekly's analysis →
  • A new arxiv preprint argues RLHF annotator responses may not represent genuine preferences at all, but responses constructed on the spot.
  • Filtering high-inconsistency annotators in two RLHF datasets flipped majority harm classifications for 18.6% of prompts.
  • The same filtering shifted mean ratings by more than 13 points on a 100-point scale, suggesting systematic rather than random noise.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

For today's reading group, @pia-p.bsky.social presented "When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning" by Yijiang River Dong et al. (2025) Paper: aclanthology.org/2025.finding... #NLProc

aclanthology.org View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#MemoryMonday #NLProc 'Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists' by Attanasio et al. redefines bias reduction in #AI, sans prior term knowledge. #2022Publication aclanthology.org/2022.finding...

Entropy-based Attention Regularization Frees Unintended Bias Mitigation from Lists aclanthology.org
AI Weekly's analysis →
  • EAR adds an objective that penalizes tokens with low self-attention entropy, discouraging BERT from overfitting to specific training terms.
  • Across three benchmark corpora in English and Italian, EAR matches or exceeds state-of-the-art for hate-speech classification and bias metrics.
  • Because the method needs no list, it also surfaces the terms most likely to induce bias as a diagnostic byproduct.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#TBT #NLProc Hessenthaler et al.'s 2022 work delves into AI's link with fairness & energy reduction in English NLP models, challenging bias reduction theories. #AI #NLP #sustainability aclanthology.org/2022.emnlp-m...

Bridging Fairness and Environmental Sustainability in Natural Language Processing aclanthology.org
AI Weekly's analysis →
  • An EMNLP 2022 paper reports that knowledge distillation, a common efficiency technique, can actually decrease model fairness rather than preserve it.
  • The case study evaluates distilled models on natural language inference and semantic similarity, with gender bias measured via the Word Embedding Association Test.
  • The authors argue fairness and environmental sustainability are studied in isolation, and that an exclusive focus on one can hinder the other.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#MemoryMonday #NLProc 'BERTective: Language Models and Contextual Information for Deception Detection' by Fornaciari, T. et al. (2021) explores AI's ability to detect deceit through context. www.aclweb.org/anthology/20...

BERTective: Language Models and Contextual Information for Deception Detection - ACL Anthology aclweb.org
AI Weekly's analysis →
  • Fornaciari, Bianchi, Poesio and Hovy combine BERT with attention over surrounding text to identify deceptive statements in Italian dialogues.
  • Only context near the target utterance helps, and only when it comes from the same speaker rather than from an interlocutor's questions.
  • The authors report a new state of the art on the task and release the dataset and code for reproducibility on GitHub.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#TBT #NLProc 'Exploring challenges in Zero-shot Cross-lingual Hate Speech Detection, @deboranozza.bsky.social (2021) reveals how current models may inaccurately label non-hateful, language-specific interjections as hate speech signals.' aclanthology.org/2021.acl-sho...

Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection aclanthology.org
AI Weekly's analysis →
  • Debora Nozza's 2021 ACL-IJCNLP short paper tests transferring an English hate speech model to Italian and Spanish without target-language labels.
  • Post-hoc explanations show the model misreads non-hateful, language-specific taboo interjections as signals of hate speech.
  • The paper concludes zero-shot cross-lingual models cannot be used as they are and need to be carefully designed.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#MemoryMonday #NLProc 'MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?' by Fornaciari et al. (2021) indicates that emotion and empathy are not related tasks for prediction. aclanthology.org/2021.wassa-1...

MilaNLP @ WASSA: Does BERT Feel Sad When You Cry? aclanthology.org
AI Weekly's analysis →
  • MilaNLP at Bocconi submitted to the WASSA 2021 shared task on essay-level emotion classification of reactions to English news stories.
  • Adding empathy as an auxiliary task and demographic attributes as input both gave worse performance than the plain single-task model.
  • The authors conclude emotion and empathy are not related tasks for prediction, despite the submission remaining competitive at the competition.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#TBT #NLProc 'Universal Joy: A Data Set and Results for Classifying Emotions Across Languages' by Lamprinidis et al. (2021) explores how AI research affects our planet. Tech can be green too! #SustainableTech aclanthology.org/2021.wassa-1.7

Universal Joy A Data Set and Results for Classifying Emotions Across Languages aclanthology.org
AI Weekly's analysis →
  • Universal Joy assembles over 530,000 anonymized public Facebook posts across 18 languages, each labeled with one of five emotions.
  • Using multilingual BERT, the authors report that emotions can be inferred both within and across languages, with typologically similar languages helping each other.
  • Zero-shot transfer to low-resource languages is reported as promising, suggesting cross-lingual emotion models can extend beyond the training-language set.
Read full analysis →
 View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

For today's reading group, Urs Zaberer presented "Toward universal steering and monitoring of AI models" by Beaglehole et al. (2026) Paper: www.science.org/doi/10.1126/... #NLProc

science.org View on Bluesky →
↻ Debora Nozza reposted
MilaNLP Lab @milanlp.bsky.social

#MemoryModay #NLProc 'Measuring Hurtful Sentence Completion in Language Models' by @debora_nozza et al. introduces HONEST, a new metric for harmful stereotypes. Language models use hurtful words 4.3% of the time.

HONEST: Measuring Hurtful Sentence Completion in Language Models aclanthology.org
AI Weekly's analysis →
  • The HONEST benchmark found language models produced a hurtful word in roughly 4.3% of sentence completions across the study.
  • When prompts targeted women, 9% of completions referenced sexual promiscuity; when they targeted men, 4% referenced homosexuality.
  • The template- and lexicon-based methodology was applied across six languages: English, Italian, French, Portuguese, Romanian, and Spanish.
Read full analysis →
 View on Bluesky →
Their network

In Debora Nozza's orbit

Center = Debora Nozza. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.