Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data. On-device ML is highly context dependent, and sensitive to user, usage, hardware and environment attributes. This sensitivity and the propensity towards bias in ML makes it important to study bias in on-device settings. Our study is one of the first investigations of bias in this emerging domain, and lays important foundations for building fairer on-device ML. We apply a software engineering lens, investigating the propagation of bias through design choices in on-device ML workflows. We first identify reliability bias as a source of unfairness and propose a measure to quantify it. We then conduct empirical experiments for a keyword spotting task to show how complex and interacting technical design choices amplify and propagate reliability bias. Our results validate that design choices made during model training, like the sample rate and input feature type, and choices made to optimize models, like light-weight architectures, the pruning learning rate and pruning sparsity, can result in disparate predictive performance across male and female groups. Based on our findings we suggest low effort strategies for engineers to mitigate bias in on-device ML.

Google held its big annual hardware event Tuesday in New York to unveil the Pixel 4, Nest Mini, Pixelbook Go, Nest Wifi, and Pixel Buds. It was mostly predictable because details about virtually every piece of hardware the company revealed at the event were leaked months in advance, but if Google's biggest hardware event of the year had an overarching theme, it was the many applications of on-device machine learning. Most of the hardware Google introduced includes a dedicated chip for running AI, continuing an industry-wide trend to power services consumers will no doubt enjoy, but there can be privacy implications too. The new Nest Mini's on-device machine learning recognizes your most commonly used voice commands to quicken Google Assistant response time compared to the first-generation Home Mini. In Pixel Buds, due out next year, machine learning helps recognize ambient sound levels and increase or decrease sound the same way your smartphone dims or brightens when it's in sunlight or shade.