In Progress
Mayer, L. W., Bocheva, D., Hinds, J., Brown, O., Piwek, L., Ellis, D. (under Review) Behavior Research Methods.
Do your studies rely on groups of people? Recruiting participants for group sessions can be arduous, often costing a lot of time, effort, and resources. In this work, we demonstrate the complete lack of tools that can appropriately allocate a sample of interested participants to a set of group sessions. We then mathematically derive metrics that can quantify the effectiveness of any tool attempting to solve this problem. Finally, we develop an algorithm that outperforms any existing utility on this task using simulation and a large-scale pre-registered user study. Our scheduling utility is free and open-source, available to anyone through a web-applet.
Mayer, L. W., Karny, S., Ayoub, J., Song, M., Tian, D., Moradi-Pari, E., Steyvers, M. (Submitted) Nature Machine Intelligence.
How can we make AI collaborate well with people? Narrowly optimizing the performance of AI agents may be convenient, but can cause frustration when people are then asked to work with this agent. In this paper, we show that people prefer AI agents that are considerate of their preferences, even when this comes at the cost of performance. We also find that certain human-centric design choices boost people’s liking of the agent, without harming the performance of the human-AI team. Our results strongly suggest that leveraging both subjective and objective metrics is crucial when designing AI agents for human collaboration.
