We work on the analysis of digital traces of human behavior to strive towards building responsible Artificial Intelligence.
Some of our research lines are the following:

Affective AI

Building AI that understands and respects human emotion.
Example publications:
A Computational Method to Reveal Psychological Constructs from Text Data. Psychological Methods
LEIA: Linguistic Embeddings for the Identification of Affect. EPJ Data Science

Complex Privacy

Understanding the emergence of new privacy risks in human-AI networks.
Example publications:
Leaking Privacy and Shadow Profiles in Online Social Networks. Science Advances
Collective aspects of privacy in the Twitter social network. EPJ Data Science

Representation in Computational Methods

Auditing how digital traces and intelligent technologies capture and create inequalities.
Example publications:
Analyzing gender inequality through large-scale Facebook advertising data. PNAS
Validating daily social media macroscopes of emotions. Scientific Reports

Modelling Collective Behavior

How collective behavior emerges in societies of humans and machines.
Example publications:
Language Understanding as a Constraint on Consensus Size in LLM Societies
Raising the Spectrum of Polarization: Generating Issue Alignment with a Weighted Balance Opinion Dynamics Model. JASSS