Carlos' (they/them pronouns) goal is to address problems of social significance through computational methods and interdisciplinary research, and their current focus is on algorithmic fairness. Carlos' background is web mining and information retrieval, and they have been influential in the areas of crisis informatics and web content quality and credibility. They are a prolific, highly cited researcher who have received two test-of-time awards, five best paper awards, and two best student paper awards. Carlos' works include a book on Big Crisis Data, as well as monographs on Information and Influence Propagation, and Adversarial Web Search. They lead the Web Science and Social Computing research group at Universitat Pompeu Fabra, and coordinate the Horizon Europe FINDHR project on non-discrimination in algorithmic hiring.
Research interests
The focus of my research is algorithmic fairness. Currently, I work on the evaluation of decision support tools for risk assessment and recruitment and on the evaluation of datasets for machine learning, from the perspective of intersectional non-discrimination.
Selected publications
– Porcaro L, Gómez E & Castillo C 2024, ‘Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study.’ ACM Transactions on Recommender Systems, Volume 2, Issue 1, Article No.: 3, Pages 1 -47.
– Portela M, Castillo C, Tolan S, Karimi-Haghighi M & Pueyo AA 2024, ‘A comparative user study of human predictions in algorithm-supported recidivism risk assessment‘, Artificial intelligence and law.
– Marques F, Hernández-Leo D & Castillo C 2024, ‘Measuring gender bias in student satisfaction in higher education: a cross-department study‘, Cogent education, 11 – 1 -2375183.
– Casanovas-Builiart L, Alvarez-Cueva P & Castillo C 2024, ‘Evolution over 62 years: an analysis of sexism in the lyrics of the most-listened-to songs in Spain‘, Cogent arts & humanities, 11 – 1 -2436723.
Selected research activities
Carlos is the coordinator of Horizon Europe project FINDHR – Fairness and Intersectional Non-Discrimination in Human Recommendation (Grant Agreement 101070212), which unites 12 partner organizations in developing anti-discrimination methods, algorithms, and training for algorithmic hiring.