Hao-Fei Cheng
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Machine learning tools have been deployed in various contexts to support human decision-making, in the hope that human-algorithm collaboration can improve decision quality. However, the question of whether such collaborations reduce or exacerbate biases in decision-making remains underexplored. In this work, we conducted a mixed-methods study, analyzing child welfare call screen workers' decision-making over a span of four years, and interviewing them on how they incorporate algorithmic predictions into their decision-making process. Our data analysis shows that, compared to the algorithm alone, call screen workers reduced the disparity in screen-in rate between Black and white children from 20\% to 9\%. Our qualitative data show that workers achieved this by making holistic risk assessments and complementing the algorithm's limitations. These results shed light on potential mechanisms for improving human-algorithm collaboration in high-risk decision-making contexts.
https://dl.acm.org/doi/abs/10.1145/3491102.3501831
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)