# Mirroring to Build Trust in Digital Assistants

## TL;DR

This paper investigates how matching a digital assistant's conversational style to the user's preferences can enhance trust and user satisfaction, supported by a user study and predictive modeling.

## Contribution

It introduces a method to predict user preferred conversational styles and demonstrates that style matching increases trust in digital assistants.

## Key findings

- Users prefer and trust assistants that match their conversational style.
- Models can reliably predict user style preferences.
- Style matching improves user satisfaction.

## Abstract

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end we conducted a user study where subjects interacted with a digital assistant that responded in a way that either matched their conversational style, or did not. Using self-reported personality attributes and subjects' feedback on the interactions, we built models that can reliably predict a user's preferred conversational style.