Understanding the Effects of AI-Assisted Critical Thinking on Human-AI Decision Making

TL;DR

This paper introduces the AI-Assisted Critical Thinking (AACT) framework that uses AI to analyze and improve human decision-making by encouraging reflection, demonstrated through a house price prediction case study.

Contribution

The paper presents a novel AI framework that promotes critical thinking in human-AI decision making by analyzing decision rationales and providing counterfactual feedback.

Findings

AACT reduces over-reliance on AI in decision making

AACT increases cognitive load for users

AACT benefits decision-makers familiar with AI technologies

Abstract

Despite the growing prevalence of human-AI decision making, the human-AI team's decision performance often remains suboptimal, partially due to insufficient examination of humans' own reasoning. In this paper, we explore designing AI systems that directly analyze humans' decision rationales and encourage critical reflection of their own decisions. We introduce the AI-Assisted Critical Thinking (AACT) framework, which leverages a domain-specific AI model's counterfactual analysis of human decision to help decision-makers identify potential flaws in their decision argument and support the correction of them. Through a case study on house price prediction, we find that AACT outperforms traditional AI-based decision-support in reducing over-reliance on AI, though also triggering higher cognitive load. Subgroup analysis reveals AACT can be particularly beneficial for some decision-makers such as those very familiar with AI technologies. We conclude by discussing the practical implications of our findings, use cases and design choices of AACT, and considerations for using AI to facilitate critical thinking.