2024/03/25 by Shuai Ma, Ma, Shuai, Qiaoyi Chen +11 · 1 voice · 22 citations
Business, Management and Accounting · Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Big Data and Business Intelligence #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #cs.AI #cs.HC
paper · pdf · doi:10.48550/arxiv.2403.16812
openalex publication_date 2024/03/25 · arxiv published 2024/03/25 · arxiv updated 2025/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. In such a paradigm, humans are found to rarely trigger analytical thinking and face difficulties in communicating the nuances of conflicting opinions to the AI when disagreements occur. To tackle this challenge, we propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates. To empower AI with deliberative capabilities, we designed Deliberative AI, which leverages large language models (LLMs) as a bridge between humans and domain-specific models to enable flexible conversational interactions and faithful information provision. An exploratory evaluation on a graduate admissions task shows that Deliberative AI outperforms conventional explainable AI (XAI) assistants in improving humans' appropriate reliance and task performance. Based on a mixed-methods analysis of participant behavior, perception, user experience, and open-ended feedback, we draw implications for future AI-assisted decision tool design.