Dario Pesenti

Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer

It is demonstrated that standard LtD strategies show class-dependent sampling bias in classification tasks in practice, and thus may disproportionately defer the minority classes when applied to imbalanced datasets, and that such asymmetries in task delegation may trigger human biases, ultimately leading to poorer downstream decision making.