Blind Users Accessing Their Training Images in Teachable Object Recognizers

Iteration of training and evaluating a machine learning model is an important process to improve its performance. However, while teachable interfaces enable blind users to train and test an object recognizer with photos taken in their distinctive environment, accessibility of training iteration and evaluation steps has received little attention. Iteration assumes visual inspection of the training photos, which is inaccessible for blind users. We explore this challenge through MyCam, a mobile app that incorporates automatically estimated descriptors for non-visual access to the photos in the users' training sets. We explore how blind participants (N=12) interact with MyCam and the descriptors through an evaluation study in their homes. We demonstrate that the real-time photo-level descriptors enabled blind users to reduce photos with cropped objects, and that participants could add more variations by iterating through and accessing the quality of their training sets. Also, Participants found the app simple to use indicating that they could effectively train it and that the descriptors were useful. However, subjective responses were not reflected in the performance of their models, partially due to little variation in training and cluttered backgrounds.

Exploring Machine Teaching with Children

Iteratively building and testing machine learning models can help children develop creativity, flexibility, and comfort with machine learning and artificial intelligence. We explore how children use machine teaching interfaces with a team of 14 children (aged 7-13 years) and adult co-designers. Children trained image classifiers and tested each other's models for robustness. Our study illuminates how children reason about ML concepts, offering these insights for designing machine teaching experiences for children: (i) ML metrics (e.g. confidence scores) should be visible for experimentation; (ii) ML activities should enable children to exchange models for promoting reflection and pattern recognition; and (iii) the interface should allow quick data inspection (e.g. images vs. gestures).