Lorenzo Carta

Pierluigi Masai, Lorenzo Carta, Mateusz Miroslaw Lis

The integration of Generative AI (GenAI) into audio-visual production is often presented as a radical break from past traditions. However, through a sociomaterial and historical lens, this paper argues that GenAI represents a new development in the long-standing negotiation between creative labor and technological possibilities. Moving beyond the limiting framework of human-machine co-creativity, we adopt an STS-based approach to investigate creativity in the making within the Filmmaking industry. We analyze Filmmaking as a distributed process where agency is shared across diverse human experts and non-human actors, showing how technological innovations have historically reconfigured Filmmaking practices long before the advent of AI. The article introduces an analytical taxonomy of GenAI techniques to illustrate how these technologies do not merely "assist" but can actively reconfigure professional roles, production temporalities, and film aesthetics. By linking sociomaterial configurations to aesthetic outcomes, this reframing suggests that AI technologies in Filmmaking should be seen as mediators that could enable new aesthetic possibilities by blurring the boundaries of traditional filmmaking workflows.

Lorenzo Carta, Fernando Spadea, Oshani Seneviratne

We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financial distress across all 50 states and the District of Columbia without centralizing sensitive data. Our cross-silo FL setup treats each state as a distinct data silo, simulating real-world governance in nationwide financial systems. Unlike prior work, our approach integrates two complementary explainable AI techniques to identify both global (nationwide) and local (state-specific) predictors of financial hardship, such as contact from debt collection agencies. We develop a machine learning model specifically suited for highly categorical, imbalanced survey data. This work delivers a scalable, regulation-compliant blueprint for early warning systems in finance, demonstrating how FL can power socially responsible AI applications in consumer credit risk and financial inclusion.