Making the Switch: Towards Intelligent Integration of Gestures As an Input Modality for Microtask Crowdsourcing

Abstract

Human input is pivotal in building AI systems. Aiding the gathering of high-quality and representative human input on demand, microtask crowdsourcing platforms have thrived. Despite the benefits available, the lack of health provisions, safeguards, and existing practices threaten the sustainability of crowd work. Prior work investigated the usefulness of a dual-purpose input modality of ergonomically-informed gestures across different microtasks, finding that gestures as inputs offer a realistic trade-off between worker accuracy and potential short to long-term health benefits. However, little is understood about the effect of switching input modalities from one task to another on worker experiences and task-related outcomes. Addressing this research and empirical gap, we conducted a between-subjects study (N = 717) with varying sequences of input modalities across 16 experimental conditions to systematically understand the effect of switching input modalities. We found that the order of the input modality can influence the time it takes to complete tasks but does not affect accuracy. Further, the cognitive load perceived by workers was not significantly different between conditions. Our findings hint that ergonomically informed gestures can be effectively intertwined with conventional input modalities without a detrimental impact on worker experiences and quality-related outcomes. Our work has important implications for the design of human-centered crowdsourcing platforms that cater to worker health and wellbeing.