Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review

TL;DR

This paper presents a taxonomy of interaction patterns in AI-assisted decision making, highlighting the dominance of simplistic collaboration paradigms and proposing a framework to enhance human-AI interaction for better communication and trust.

Contribution

It introduces a comprehensive taxonomy of human-AI interaction patterns based on a systematic review of 105 articles, addressing the lack of a common vocabulary for interaction protocols.

Findings

Current interactions are mostly simplistic with limited interactivity

The taxonomy helps understand and improve human-AI communication

Opportunities exist to develop more collaborative AI-assisted decision-making

Abstract

Leveraging Artificial Intelligence (AI) in decision support systems has disproportionately focused on technological advancements, often overlooking the alignment between algorithmic outputs and human expectations. A human-centered perspective attempts to alleviate this concern by designing AI solutions for seamless integration with existing processes. Determining what information AI should provide to aid humans is vital, a concept underscored by explainable AI's efforts to justify AI predictions. However, how the information is presented, e.g., the sequence of recommendations and solicitation of interpretations, is equally crucial as complex interactions may emerge between humans and AI. While empirical studies have evaluated human-AI dynamics across domains, a common vocabulary for human-AI interaction protocols is lacking. To promote more deliberate consideration of interaction designs, we introduce a taxonomy of interaction patterns that delineate various modes of human-AI interactivity. We summarize the results of a systematic review of AI-assisted decision making literature and identify trends and opportunities in existing interactions across application domains from 105 articles. We find that current interactions are dominated by simplistic collaboration paradigms, leading to little support for truly interactive functionality. Our taxonomy offers a tool to understand interactivity with AI in decision-making and foster interaction designs for achieving clear communication, trustworthiness, and collaboration.