Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise

TL;DR

This pilot study investigates how domain expertise influences trust in prediction models, revealing that trust is multifaceted and not solely determined by expertise, through a mixed-methods exploration of user interactions with a visual analytics system.

Contribution

The study provides new insights into the complex factors affecting trust in prediction models and highlights the limitations of domain expertise as a sole predictor.

Findings

Trust is influenced by multiple factors beyond domain expertise.

Six themes affecting trust were identified.

Trust evolves during user exploration of models.

Abstract

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We share our results and experiences of an exploratory pilot study in which four people experienced with predictive modelling systematically explore a visual analytics system with an unknown prediction model. Through a mixed-methods approach involving Likert-type questions and a semi-structured interview, we investigate how people's trust evolves during their exploration, and we distil six themes that affect their trust in the prediction model. Our results underline the multi-faceted nature of trust, and suggest that domain expertise alone cannot fully predict people's trust perceptions.