Results of the Mann-Whitney U test and the t-test comparing boundary shift for the three forms of iterated algorithmic bias with class-dependent human action probability ratio 10:1.

The effect size is calculated as (Boundary|t = 0 − Boundary|t = 200)/standard.dev. The negative effect size for filter bias shows that it decreases the number of points which are predicted to be in class y = 1. Random selection increases the number of points, while active learning does not have a significant effect.