The Rich Get Richer: Disparate Impact of Semi-Supervised Learning. (arXiv:2110.06282v3 [cs.LG] UPDATED)

Semi-supervised learning (SSL) has demonstrated its potential to improve the
model accuracy for a variety of learning tasks when the high-quality supervised
data is severely limited. Although it is often established that the average
accuracy for the entire population of data is improved, it is unclear how SSL
fares with different sub-populations. Understanding the above question has
substantial fairness implications when different sub-populations are defined by
the demographic groups that we aim to treat fairly. In this paper, we reveal …

arxiv impact learning lg semi-supervised semi-supervised learning supervised learning