Whether to trust: the ML leap of faith

TL;DR

This paper introduces a novel neuro-symbolic framework to measure and build intrinsic trust in ML systems by analyzing the 'leap of faith' users take, using a matrix that compares ML models with expert mental models, supported by empirical field study.

Contribution

It proposes a new method to directly measure and manage trust in ML by quantifying the leap of faith through the LoF matrix, bridging the gap between user mental models and ML systems.

Findings

LoF matrix effectively captures trust gaps between users and ML.

Trust metrics based on actions outperform self-reports.

Empirical field study demonstrates practical applicability.

Abstract

Human trust is a prerequisite to trustworthy AI adoption, yet trust remains poorly understood. Trust is often described as an attitude, but attitudes cannot be reliably measured or managed. Additionally, humans frequently conflate trust in an AI system, its machine learning (ML) technology, and its other component parts. Without fully understanding the 'leap of faith' involved in trusting ML, users cannot develop intrinsic trust in these systems. A common approach to building trust is to explain a ML model's reasoning process. However, such explanations often fail to resonate with non-experts due to the inherent complexity of ML systems and explanations are disconnected from users' own (unarticulated) mental models. This work puts forward an innovative way of directly building intrinsic trust in ML, by discerning and measuring the Leap of Faith (LoF) taken when a user decides to rely on ML. The LoF matrix captures the alignment between an ML model and a human expert's mental model. This match is rigorously and practically identified by feeding the user's data and objective function into both an ML agent and an expert-validated rules-based agent: a verified point of reference that can be tested a priori against a user's own mental model. This represents a new class of neuro-symbolic architecture. The LoF matrix reveals to the user the distance that constitutes the leap of faith between the rules-based and ML agents. For the first time, we propose trust metrics that evaluate whether users demonstrate trust through their actions rather than self-reported intent and whether such trust is deserved based on outcomes. The significance of the contribution is that it enables empirical assessment and management of ML trust drivers, to support trustworthy ML adoption. The approach is illustrated through a long-term high-stakes field study: a 3-month pilot of a multi-agent sleep-improvement system.