Core Concepts
Designing effective human-AI interactions is crucial for decision-making tasks.
Abstract
The content discusses the importance of human-centered AI solutions in decision-making processes. It introduces a taxonomy of interaction patterns based on a systematic review of 105 articles. The taxonomy categorizes various modes of human-AI interactivity to promote clear communication, trustworthiness, and collaboration. The article highlights the dominance of simplistic collaboration paradigms and the need for more interactive functionality in current interactions.
INTRODUCTION
Leveraging AI in decision support systems.
Human-AI teamwork dynamics.
Importance of human-centered AI solutions.
METHODS
Search strategy and selection criteria.
Study selection process.
Data extraction strategy.
RESULTS
Taxonomy of interaction patterns for AI-assisted decision making.
Identification of interaction patterns across different domains.
Evaluation methods and measures used in studies.
DISCUSSION
Challenges and opportunities in designing effective human-AI interactions.
Variability in interaction patterns across domains.
Importance of considering user psychology and biases in interaction design.
Stats
"105 articles" "25 pages" "Under submission, 2024"