## 2 Materials and Methods
### Experimental Design and Data Collection  
We adopted a within-subject design in this study. Each participant was asked to perform the decision-making task twice for each case, both before and after the CDSS prediction and explanation were presented, as described in Section 2.5. Four different, yet comparable cases were selected for the survey; these cases emanated from the independent test set in the development of the CDSS (see [22]). These four cases were women with BMI 25-39.9 kg/m\({}^{2}\) recruited at the National maternity Hospital in Dublin, Ireland between 2013 and 2016 in the PEARS study [23]. Two of them were correctly predicted by our CDSS and two were incorrectly predicted, for the purpose of identifying any self-reliance or over-reliance on the CDSS. These cases were confirmed to be analogous by a qualified obstetrician, and were grouped in pairs.  
For the comparison of the two types of explanations, one group of correct and incorrect cases were explained by feature contribution, and the other group by example, randomly assigned for each participant. The order of the correctly and incorrectly predicted cases in each group was also randomized in order to prevent order effect. Due to time restrictions,  
Figure 4: CDSS prediction and explanation by example for an exemplar case.  
we did not ask participants to interact with the CDSS prototype directly. We presented the cases, predictions of our CDSS and explanations in text and figures in the Google form.  
To measure the impact of CDSS predictions and explanations on participants' GDM risk estimation, we used the weight of advice (WOA) as a dependent variable. WOA is defined as: \(WOA=\frac{|final\ estimate-initial\ estimate|}{|advice-initial\ estimate|}\). It measures the percentage shift in judgment after advice, and it quantifies how much the participants follow the advice they receive. It was employed in several advice-taking studies [26; 27], and it was previously used to measure healthcare providers' advice-taking from an AI-based CDSS [15]. In this study, the initial and final estimates are participants' estimation of the GDM risk before and after the CDSS prediction and explanation, on the scale of 0-100%. The CDSS gives binary classification, predicting whether a case is at low risk or high risk of GDM. Therefore, the advice is either 0% or 100%. WOA ranges from 0 to 1, where 1 means a participant follow the prediction of our CDSS completely and 0 means the participant does not take any advice from our CDSS.  
At the end of the survey, participants were asked which explanation or combination they preferred. They were asked to leave any comments (optional). They were also asked about their clinical expertise (type of healthcare practitioner, years of experience), their prior experience of CDSS, and their inclination towards CDSS use (11-point Likert scale, from 0 = \"Not at all inclined\" to 10 = \"Extremely inclined\").  
The survey (see Form S1) was adapted from what was described in [15] with changes, and was carefully designed in consultation with computer science experts and qualified healthcare practitioners. All names in the survey are pseudonyms. The survey was tested by qualified obstetricians before dissemination.