Alessandra Costantino
I am a researcher with a strong track record of transferring core scientific computing skills across very different technical and scientific backgrounds ranging from radiation detection and medical physics to Earth observation. I have worked across disciplines in academic and industry settings and am particularly drawn to complex problems that require continuous learning and close collaboration across different domains.
Session
Explainable AI is frequently invoked to make machine learning systems understandable and trustworthy. In real applications, however, explanations are often expected to justify decisions and support action. Drawing on experience with remote sensing–based risk monitoring, this talk examines the gap between the guarantees of explainability methods and the expectations placed on them by different users. It discusses how explanations can inform practice, how they can be misinterpreted, and when focusing on explainability may obscure deeper problems in models or data.