The Impact and Feasibility of Self-Confidence Shaping for AI-Assisted Decision-Making

TL;DR

This paper explores a human-centered intervention to calibrate self-confidence in AI-assisted decision-making, demonstrating significant performance improvements and predictive modeling for effective collaboration.

Contribution

It introduces a novel self-confidence shaping intervention, quantifies its impact on team performance, and develops models to predict when intervention is needed.

Findings

Self-confidence shaping improves human-AI team performance by nearly 50%.

Simple machine learning models achieve 67% accuracy in predicting self-confidence.

Sentiment analysis suggests modifying sentiment could help calibrate self-confidence.

Abstract

In AI-assisted decision-making, it is crucial but challenging for humans to appropriately rely on AI, especially in high-stakes domains such as finance and healthcare. This paper addresses this problem from a human-centered perspective by presenting an intervention for self-confidence shaping, designed to calibrate self-confidence at a targeted level. We first demonstrate the impact of self-confidence shaping by quantifying the upper-bound improvement in human-AI team performance. Our behavioral experiments with 121 participants show that self-confidence shaping can improve human-AI team performance by nearly 50% by mitigating both over- and under-reliance on AI. We then introduce a self-confidence prediction task to identify when our intervention is needed. Our results show that simple machine-learning models achieve 67% accuracy in predicting self-confidence. We further illustrate the feasibility of such interventions. The observed relationship between sentiment and self-confidence suggests that modifying sentiment could be a viable strategy for shaping self-confidence. Finally, we outline future research directions to support the deployment of self-confidence shaping in a real-world scenario for effective human-AI collaboration.