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

Catalina Gomez, Sue Min Cho, Shichang Ke, Chien-Ming Huang, Mathias Unberath

TL;DR

A taxonomy of interaction patterns that delineate various modes of human-AI interactivity is introduced, offering a tool to understand interactivity with AI in decision-making and foster interaction designs for achieving clear communication, trustworthiness, and collaboration.

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.

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

TL;DR

A taxonomy of interaction patterns that delineate various modes of human-AI interactivity is introduced, offering a tool to understand interactivity with AI in decision-making and foster interaction designs for achieving clear communication, trustworthiness, and collaboration.

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.
Paper Structure (30 sections, 3 figures, 1 table)

This paper contains 30 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Search strategy and selection criteria
  4. Study selection
  5. Data extraction strategy
  6. Analysis process
  7. Context
  8. AI system
  9. Evaluation
  10. Interaction building blocks
  11. Results
  12. Taxonomy of interaction patterns for AI-assisted decision making
  13. AI-first assistance
  14. AI-follow assistance
  15. Secondary assistance
  16. ...and 15 more sections

Figures (3)

  • Figure 1: Taxonomy of interaction patterns identified in AI-assisted decision making. The user (human) and the AI are represented as separate agents and the temporal evolution of the interactions is illustrated from top to down. The boxes contain the building blocks (action-output) pairs that compose each pattern. The direction of arrows denote the agent who started the action. Dashed lines represent optional operations.
  • Figure 2: Percentage of interaction patterns observed in each domain of AI-assisted decision making tasks included in this survey. The numbers in the cells denote the percentage values (e.g., 17% of the patterns identified in the healthcare domain correspond to Secondary assistance). One study can include multiple sequences of interaction and interaction patterns are not mutually exclusive.
  • Figure 3: PRISMA diagram for article selection in this survey.