David Casacuberta is a tenured lecturer in the Department of Philosophy at the Universitat Autònoma de Barcelona. His current line of research focuses on the social and cognitive impacts of artificial intelligence, especially in the fields of health and biomedicine.
At a methodological level, his research centres on how to detect and minimise biases in biomedicine that result from the use of artificial intelligence algorithms, as well as the potential discrimination against minorities that may arise from such biases. This analysis is based, on the one hand, on a detailed understanding of how machine learning algorithms work and how to equip them with explainability mechanisms, and on the other hand, on an analysis of the epistemic foundations of public health in order to establish commonalities and divergences between statistical population analyses and the results generated by machine learning algorithms.
Likewise, David Casacuberta works on protocols for transferring theoretical results to potential applications in different fields. In this regard, he has taken part in various conferences and masterclasses aimed at professionals in artificial intelligence, public health, interface design, and health administration, notably contributing to the hybrid (online + in-person sessions) course on Big Data and Artificial Intelligence for healthcare personnel, offered by Barcelona Bioinformatics and aimed at public health and hospital managers from Spain’s autonomous communities. In this course, he developed the ethics and epistemology materials on AI and presented them during the in-person sessions.

David Casacuberta is a tenured lecturer in the Department of Philosophy at the Universitat Autònoma de Barcelona. His current line of research focuses on the social and cognitive impacts of artificial intelligence, especially in the fields of health and biomedicine.
At a methodological level, his research centres on how to detect and minimise biases in biomedicine that result from the use of artificial intelligence algorithms, as well as the potential discrimination against minorities that may arise from such biases. This analysis is based, on the one hand, on a detailed understanding of how machine learning algorithms work and how to equip them with explainability mechanisms, and on the other hand, on an analysis of the epistemic foundations of public health in order to establish commonalities and divergences between statistical population analyses and the results generated by machine learning algorithms.
Likewise, David Casacuberta works on protocols for transferring theoretical results to potential applications in different fields. In this regard, he has taken part in various conferences and masterclasses aimed at professionals in artificial intelligence, public health, interface design, and health administration, notably contributing to the hybrid (online + in-person sessions) course on Big Data and Artificial Intelligence for healthcare personnel, offered by Barcelona Bioinformatics and aimed at public health and hospital managers from Spain’s autonomous communities. In this course, he developed the ethics and epistemology materials on AI and presented them during the in-person sessions.