Sibyl: Explaining Machine Learning Models for High-Stakes Decision Making
This paper, presented at the CHI Conference on Human Factors in Computing Systems (May 2021), addresses the need for an effective way to explain machine learning predictions in the domain of child welfare screening. In child welfare, machine learning can be used to generate useful insights from the large amount of data available to screeners, potentially improving the outcomes for children referred for alleged abuse and neglect. Through a series of interviews and user studies, the authors developed Sibyl, a machine learning explanation dashboard. They tested four different explanation types and decided a local feature contribution approach was most useful to screeners.
Zytek, A., Liu, D., Vaithianathan, R., & Veeramachaneni, K. (2021, May). Sibyl: Explaining Machine Learning Models for High-Stakes Decision Making. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-6).