Data Labeling

Measure of consistency across labelers; low agreement indicates ambiguous tasks or poor guidelines.

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.

Ordering training samples from easier to harder to improve convergence or generalization.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Local surrogate explanation method approximating model behavior near a specific input.

Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and e...

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Controlled experiment comparing variants by random assignment to estimate causal effects of changes.