Designing Interactive Transfer Learning Tools for ML Non-Experts


Interactive machine learning (iML) tools help to make ML accessible to users with limited ML expertise. However, gathering necessary training data and expertise for model-building remains challenging. Transfer learning, a process where learned representations from a model trained on potentially terabytes of data can be transferred to a new, related task, offers the possibility of providing "building blocks" for non-expert users to quickly and effectively apply ML in their work. However, transfer learning largely remains an expert tool due to its high complexity. In this paper, we design a prototype to understand non-expert user behavior in an interactive environment that supports transfer learning. Our findings reveal a series of data- and perception-driven decision-making strategies non-expert users employ, to (in)effectively transfer elements using their domain expertise. Finally, we synthesize design implications which might inform future interactive transfer learning environments.

Best Paper
Swati Mishra
Cornell University, Ithaca, New York, United States
Jeffrey M. Rzeszotarski
Cornell University, Ithaca, New York, United States




会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: UX and Interaction Design and Research: Techniques, Insights & Prototyping / Reflection, Behavior, Change & Learning

[C] Paper Room 13, 2021-05-14 09:00:00~2021-05-14 11:00:00 / [A] Paper Room 13, 2021-05-13 17:00:00~2021-05-13 19:00:00 / [B] Paper Room 13, 2021-05-14 01:00:00~2021-05-14 03:00:00
Paper Room 13
13 件の発表
2021-05-14 09:00:00
2021-05-14 11:00:00