The Impact of Expertise in the Loop for Exploring Machine Rationality

Abstract

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.

References in corpus (6)

• Expanding Explainability: Towards Social Transparency in AI systems
• Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques
• Sequential Gallery for Interactive Visual Design Optimization
• The reparameterization trick for acquisition functions
• The Human in the Infinite Loop: A Case Study on Revealing and Explaining Human-AI Interaction Loop Failures
• Trust in Prediction Models: a Mixed-Methods Pilot Study on the Impact of Domain Expertise