Human Expectations and Perceptions of Learning in Machine Teaching

Abstract

Interactive interfaces in tandem with Machine Learning (ML) models support user understanding of model uncertainty, build confidence, improve predictive accuracy and enable users to teach application-specific concepts that are difficult for the model to learn otherwise.These systems offer empirically proven benefits due to tightly coupled feedback loops and workflow scaffolding.However, deployment with ML non-experts who cannot manage the complex, expertise-heavy process remains challenging.Through deployment with non-expert users in a common classification task, we investigate the impact of human factors of machine teaching interfaces such as user expectations, their perceptions of the learning process and user engagement with respect to teaching process and outcomes.We measure how affective and performance attributes shape the success or failure of the process.Finally, we reflect on how intelligent user interfaces can be designed to accommodate these factors for successful deployment with a broad spectrum of human adjudicators.