Writings and Research
A selection of papers and preprints on how algorithms shape behavior, attention, and well-being.
VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health
VERA-MH is compared to practicing clinicians
Luca Belli
VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health
VERA-MH is compared to practicing clinicians
Luca BelliAlgorithmic amplification of politics on Twitter
Based on a massive-scale experiment involving millions of Twitter users, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left.
tech support
From Optimizing Engagement to Measuring Value
Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of “value” that is worth optimizing for.
Luca Belli
Assessing demographic bias in named entity recognition
Named Entity Recognition (NER) is often the first step towards automated Knowledge Base (KB) generation from raw text. In this work, we assess the bias in various Named Entity Recognition (NER) systems for English across different demographic groups with synthetically generated corpora.
Luca Belli