Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks

TL;DR

This study explores how different strategy recommendation modalities in human-AI collaboration are perceived by users with various personality traits, revealing key factors that influence user preferences and perceived system intelligence.

Contribution

It uniquely compares multiple recommendation schemes simultaneously in a human-subjects experiment, highlighting the impact of personality traits on preferences and perceptions.

Findings

Conscientiousness significantly affects preference for recommendation types.

Higher perceived alignment increases perceived intelligence.

Greater alignment correlates with higher usability.

Abstract

Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by understanding user preferences and recommending different strategies for solving the particular task to humans. Prior work has focused on personalization of recommendation systems for relatively well-understood tasks in the context of e-commerce or social networks. In this paper, we seek to understand the important factors to consider while designing user-centric strategy recommendation systems for decision-making. We conducted a human-subjects experiment (n=60) for measuring the preferences of users with different personality types towards different strategy recommendation systems. We conducted our experiment across four types of strategy recommendation modalities that have been established in prior work: (1) Single strategy recommendation, (2) Multiple similar recommendations, (3) Multiple diverse recommendations, (4) All possible strategies recommendations. While these strategy recommendation schemes have been explored independently in prior work, our study is novel in that we employ all of them simultaneously and in the context of strategy recommendations, to provide us an in-depth overview of the perception of different strategy recommendation systems. We found that certain personality traits, such as conscientiousness, notably impact the preference towards a particular type of system (p < 0.01). Finally, we report an interesting relationship between usability, alignment and perceived intelligence wherein greater perceived alignment of recommendations with one's own preferences leads to higher perceived intelligence (p < 0.01) and higher usability (p < 0.01).