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Understanding the Usability Challenges of Machine Learning In High-Stakes Decision Making

Fig. 1. The Sibyl "Case-Specific Details" interface. Sibyl was designed to gain concrete feedback on possible methods of addressing ML usability challenges faced by child welfare screeners. The Case-Specific Details interface is one of the ML augmentation approaches evaluated in this paper, and shows how particular factors contribute to predictions made by ML models about child welfare. (Note that we refer to features as “factors,” as this language is more familiar to our users.) Labeled elements are as follows: 1) The risk score for the case (1-20). 2) Categories for each factor, such as demographics (DG) or referral history. 3) A short description of each factor. 4) The value of numeric or categorical factors. 5) The contribution of each factor (the table can be sorted in ascending or descending order of contribution). 6) UI components for searching by factor name or filtering by category, enabled when the full factor list is shown. 7) A button for switching between a view that shows only the top 10 most contributing factors and one that shows all factors. 8) A button for switching between a single-table view and a side-by-side view, which splits factors that increase and decrease risk. 9) A sidebar that includes other explanation types, as described in this paper.