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Artificial Intelligence is being employed by humans to collaboratively solvecomplicated tasks for search and rescue, manufacturing, etc. Efficient teamworkcan be achieved by understanding user preferences and recommending differentstrategies for solving the particular task to humans. Prior work has focused onpersonalization of recommendation systems for relatively well-understood tasksin the context of e-commerce or social networks. In this paper, we seek tounderstand the important factors to consider while designing user-centricstrategy recommendation systems for decision-making. We conducted ahuman-subjects experiment (n=60) for measuring the preferences of users withdifferent personality types towards different strategy recommendation systems.We conducted our experiment across four types of strategy recommendationmodalities that have been established in prior work: (1) Single strategyrecommendation, (2) Multiple similar recommendations, (3) Multiple diverserecommendations, (4) All possible strategies recommendations. While thesestrategy recommendation schemes have been explored independently in prior work,our study is novel in that we employ all of them simultaneously and in thecontext of strategy recommendations, to provide us an in-depth overview of theperception of different strategy recommendation systems. We found that certainpersonality traits, such as conscientiousness, notably impact the preferencetowards a particular type of system (p < 0.01). Finally, we report aninteresting relationship between usability, alignment and perceivedintelligence wherein greater perceived alignment of recommendations with one'sown preferences leads to higher perceived intelligence (p < 0.01) and higherusability (p < 0.01).

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