Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making

TL;DR

This paper introduces a new measurement framework for appropriate reliance in human-AI decision-making, aiming to improve how humans discern and act on AI advice quality.

Contribution

It proposes a two-dimensional metric for assessing humans' ability to distinguish AI advice quality and behave accordingly, filling a gap in current research.

Findings

Developed a measurement concept for appropriate reliance (AR)

Illustrated application of the AR measurement framework

Outlined future research directions for human-AI decision-making

Abstract

Many important decisions in daily life are made with the help of advisors, e.g., decisions about medical treatments or financial investments. Whereas in the past, advice has often been received from human experts, friends, or family, advisors based on artificial intelligence (AI) have become more and more present nowadays. Typically, the advice generated by AI is judged by a human and either deemed reliable or rejected. However, recent work has shown that AI advice is not always beneficial, as humans have shown to be unable to ignore incorrect AI advice, essentially representing an over-reliance on AI. Therefore, the aspired goal should be to enable humans not to rely on AI advice blindly but rather to distinguish its quality and act upon it to make better decisions. Specifically, that means that humans should rely on the AI in the presence of correct advice and self-rely when confronted with incorrect advice, i.e., establish appropriate reliance (AR) on AI advice on a case-by-case basis. Current research lacks a metric for AR. This prevents a rigorous evaluation of factors impacting AR and hinders further development of human-AI decision-making. Therefore, based on the literature, we derive a measurement concept of AR. We propose to view AR as a two-dimensional construct that measures the ability to discriminate advice quality and behave accordingly. In this article, we derive the measurement concept, illustrate its application and outline potential future research.