Statistical Study of Bias in Generalized Zero-Shot Learning via Handwriting Recognition

A new arXiv paper cross-listed in AI and machine learning introduces a statistical framework for examining bias in generalized zero-shot learning, where models must recognize classes that never appeared in training. The authors ground the analysis in handwriting recognition, a setting where skewed distributions can disproportionately affect underrepresented groups. The work aims to extend bias measurement beyond the relatively narrow conditions covered by traditional GZSL methods.