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Human-AI Collaboration in Decision Making: Interaction Patterns Taxonomy


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"
Quotes

Key Insights Distilled From

by Catalina Gom... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2310.19778.pdf
Human-AI collaboration is not very collaborative yet

Deeper Inquiries

How can the taxonomy of interaction patterns be applied to real-world decision-making scenarios?

The taxonomy of interaction patterns can be applied to real-world decision-making scenarios by providing a structured framework for understanding and designing human-AI interactions. By categorizing different modes of interactivity, such as AI-first assistance, AI-follow assistance, Secondary assistance, Request-driven AI assistance, etc., the taxonomy offers a systematic way to analyze and implement these interactions in practical applications. For example, in healthcare settings where high-stakes decisions are made based on AI recommendations, understanding which interaction pattern is most effective can improve the decision-making process. By identifying the most suitable interaction pattern for specific tasks or domains, practitioners can enhance communication between humans and AI systems.

What are the implications of biases like confirmation bias and anchoring bias on user decisions with AI assistance?

Biases like confirmation bias and anchoring bias can significantly impact user decisions when interacting with AI systems. Confirmation bias refers to individuals seeking out information that confirms their preexisting beliefs or hypotheses while ignoring contradictory evidence. In the context of human-AI collaborations, users may tend to accept or reject AI recommendations based on their initial assumptions rather than objectively evaluating them. This could lead to suboptimal decision-making outcomes if users do not critically assess the validity of AI suggestions. Anchoring bias occurs when individuals rely too heavily on initial information (the "anchor") when making subsequent judgments or decisions. With respect to user decisions with AI assistance, anchoring bias may manifest as users being overly influenced by the first piece of information presented by an AI system without adequately considering alternative perspectives or additional data points. This could result in users either underutilizing or over-relying on AI advice without engaging in thorough deliberation. Both biases highlight the importance of designing human-AI interactions that mitigate cognitive pitfalls such as biased decision-making processes. Strategies like incorporating diverse perspectives into decision-making tasks, encouraging critical thinking about both human and machine-generated insights, and promoting continuous feedback loops can help counteract these biases during collaborative endeavors.

How can the findings from experimental evaluations be translated into practical applications for enhancing human-AI collaborations?

The findings from experimental evaluations play a crucial role in informing practical applications aimed at enhancing human-AI collaborations across various domains. These findings provide valuable insights into how different interaction patterns influence user behavior and decision-making processes when utilizing AI support. Designing User-Centric Interfaces: Practical applications derived from experimental evaluations can focus on designing user-centric interfaces that facilitate seamless communication between humans and AIs. Tailored Decision Support Systems: Based on empirical data gathered from experiments, developers can create tailored decision support systems that align with users' cognitive processes and preferences. Mitigating Biases: Insights gained from experimental evaluations enable developers to identify potential biases such as confirmation bias or anchoring bias within human-AI interactions and implement strategies to mitigate their effects. Continuous Improvement: By translating research findings into actionable steps for application development teams, organizations can continuously improve collaboration dynamics between humans and AIs through iterative design enhancements informed by empirical data. By leveraging these research-based insights effectively in practical settings through thoughtful implementation strategies informed by empirical evidence obtained from experiments conducted within controlled environments will ultimately lead towards more effective Human-AI collaborations benefiting end-users across diverse contexts.
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