Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions"
Abstract
This is an extended analysis of our paper "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions," which looks at racial disparities in the Allegheny Family Screening Tool, an algorithm used to help child welfare workers decide which families the Allegheny County child welfare agency (CYF) should investigate. On April 27, 2022, Allegheny County CYF sent us an updated dataset and pre-processing steps. In this extended analysis of our paper, we show the results from re-running all quantitative analyses in our paper with this new data and pre-processing. We find that our main findings in our paper were robust to changes in data and pre-processing. Particularly, the Allegheny Family Screening Tool on its own would have made more racially disparate decisions than workers, and workers used the tool to decrease those algorithmic disparities. Some minor results changed, including a slight increase in the screen-in rate from before to after the implementation of the AFST reported our paper.
Keywords
Cite
@article{arxiv.2204.13872,
title = {Extended Analysis of "How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions"},
author = {Logan Stapleton and Hao-Fei Cheng and Anna Kawakami and Venkatesh Sivaraman and Yanghuidi Cheng and Diana Qing and Adam Perer and Kenneth Holstein and Zhiwei Steven Wu and Haiyi Zhu},
journal= {arXiv preprint arXiv:2204.13872},
year = {2022}
}