On the Definition of Appropriate Trust and the Tools that Come with it

TL;DR

This paper explores the concept of appropriate trust in human-AI interactions, proposing a novel evaluation approach that leverages similarities with model performance metrics to better assess user trust and explanation effectiveness.

Contribution

It introduces a new method for evaluating appropriate trust by utilizing the parallels between trust definitions and model performance evaluation.

Findings

Proposes evaluation methods for user performance in trust assessment.

Suggests a way to measure uncertainty and trust in regression tasks.

Highlights the importance of explanation quality in trust detection.

Abstract

Evaluating the efficiency of human-AI interactions is challenging, including subjective and objective quality aspects. With the focus on the human experience of the explanations, evaluations of explanation methods have become mostly subjective, making comparative evaluations almost impossible and highly linked to the individual user. However, it is commonly agreed that one aspect of explanation quality is how effectively the user can detect if the predictions are trustworthy and correct, i.e., if the explanations can increase the user's appropriate trust in the model. This paper starts with the definitions of appropriate trust from the literature. It compares the definitions with model performance evaluation, showing the strong similarities between appropriate trust and model performance evaluation. The paper's main contribution is a novel approach to evaluating appropriate trust by taking advantage of the likenesses between definitions. The paper offers several straightforward evaluation methods for different aspects of user performance, including suggesting a method for measuring uncertainty and appropriate trust in regression.