Speaker
Description
In this talk, I ask whether and how the use of machine learning (ML) in contemporary science affects the scientific realism debate. After discussing the current state of the debate, I focus on the case of exoplanet detection, where ML has recently been used to distinguish transit signals produced by planets from false positives. I ask, first, whether ML can provide a methodologically distinct route that converges with established statistical approaches such as vespa, thereby strengthening the no-coincidence argument for realism; and second, whether ML-based anomaly detection can make exoplanet science less vulnerable to the problem of unconceived alternatives by identifying cases that fall outside established categories. The responses to these questions point toward a cautious optimism about the prospects for realism.