Gene A Pennello — accepted AI-conference papers

2024

Is this model reliable for everyone? Testing for strong calibration

Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene A Pennello, Berkman Sahiner

AISTATS 2024poster

In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous populations and satisfy strong notions of algorithmic fairness. However, the task of auditing a model for strong calibra…

Cited by 5 Source ↗PDF ↗code ↗Scholar ↗

2024

Monitoring machine learning-based risk prediction algorithms in the presence of performativity

Jean Feng, Alexej Gossmann, Gene A Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio

AISTATS 2024poster

Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of performativity: when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very t…

Cited by 8 Source ↗PDF ↗Scholar ↗