The Impact of Expertise in the Loop for Exploring Machine Rationality
This work highlights the importance of considering user expertise in designing human-in-the-loop systems, which is incremental to prior research focused on novices.
The study investigated how varying levels of human expertise affect outcome quality and satisfaction in human-in-the-loop optimization across text, photo, and 3D mesh contexts, finding that novices can achieve expert-level quality but experts conduct more iterations with lower satisfaction.
Human-in-the-loop optimization utilizes human expertise to guide machine optimizers iteratively and search for an optimal solution in a solution space. While prior empirical studies mainly investigated novices, we analyzed the impact of the levels of expertise on the outcome quality and corresponding subjective satisfaction. We conducted a study (N=60) in text, photo, and 3D mesh optimization contexts. We found that novices can achieve an expert level of quality performance, but participants with higher expertise led to more optimization iteration with more explicit preference while keeping satisfaction low. In contrast, novices were more easily satisfied and terminated faster. Therefore, we identified that experts seek more diverse outcomes while the machine reaches optimal results, and the observed behavior can be used as a performance indicator for human-in-the-loop system designers to improve underlying models. We inform future research to be cautious about the impact of user expertise when designing human-in-the-loop systems.