2025
Algorithmic Accountability in Small Data: Sample-Size-Induced Bias Within Classification Metrics
AISTATS 2025poster
Evaluating machine learning models is crucial not only for determining their technical accuracy but also for assessing their potential societal implications. While the potential for low-sample-size bias in algorithms is well known, we demonstrate the significance of sample-size bias induced by combi…