Dataset Handle: 20.500.14123/1742

Supplementary Material for the Paper "Opening the black box - Effects of decision transparency on therapists' trust in and intended use of AI-based decision support systems in ICBTs"

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Chronological data

Date of availability in catalog2025-01-23
Available from / since 2025-01-23

Language of the resource

English

Related external resources

Supplement to Zantvoort, K., Bjurner, P., Forsell, E., Wallert, J., Funk, B., & Kaldo, V. (Working Paper). Opening the black box - Effects of decision transparency on therapists’ trust in and intended use of AI-based decision support systems in ICBTs.

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Abstract

This study investigated the impact of local SHAP (SHapley Additive exPlanation) values on therapists’ perceptions of a Machine Learning-based Decision Support Tools (DST) in Internet-based Cognitive Behavioural Therapy (ICBT). The randomised experiment included 35 Swedish ICBT therapists who were each presented with a DST with six exemplary patient cases with SHAP values and six without them. The study measured therapists' understanding, trust, and perception of clinical usefulness through self-ratings at multiple stages. The primary hypothesis that adding SHAP values increased self-reported trust was confirmed by the findings (p=0.01, d=0.43 [0.07-0.76]). Further, the results suggested increased understanding, agreeance with, and the perceived usefulness of the DST predictions through SHAP values.

Resource type

Dataset

Kinds of Data

Test Data (i.e. Educational, Psychical or Psychological Measurements)
Context Materials / Supporting information

Methods

Experiment (Web-based)
Programming / Script-based data collection
Description

Thematic classification

Digital Health

Keywords

Maschinelles Lernen; Data Science; Therapeutik; Kognitives Verhalten; Verhaltenstherapie; Entscheidungsfindung; Internetbasiert; Digitale Gesundheit; Mentale Gesundheit; Intervention; Machine Learning; Data Science; Therapeutics; Cognitive Behavior; Behavioral Therapy; Decision Making; Internet-based; Digital Health; Mental Health; Intervention