(Un)fair devices: Moving beyond AI accuracy in personal sensing
It addresses bias in AI for personal sensing, which impacts health and lifestyle outcomes for users, but is incremental as it synthesizes prior work and proposes guidelines rather than introducing new methods.
This literature review tackles the problem of hidden biases in machine learning models deployed on personal devices, such as racial bias in pulse oximeters and sex bias in audio-based diagnostics, and advocates for a shift from performance-oriented evaluations to human-centered assessments to address these issues.
Personal devices are omnipresent in our lives, seamlessly monitoring our activities, from smart rings tracking sleep patterns to smartwatches keeping an eye on missed heartbeats. The rich data streams from such devices fuel advanced Artificial Intelligence (AI) applications. Instead of solely relying on direct sensor measurements, these applications are increasingly leveraging Machine Learning (ML) model estimates to derive insights. But are these estimates biased or not? This literature review delivers compelling evidence about the impact of hidden biases that creep into ML models deployed on personal devices. We discuss critical bias issues drawn from prior work such as racial bias in pulse oximeters, weight bias in optical heart rate sensors, and sex bias in audio-based diagnostics. In response to these challenges, we advocate for a shift from prioritizing performance-oriented evaluations of personal devices to adopting assessments grounded in a human-centered approach. To facilitate this transition, we provide guidelines for the design, development, evaluation, and use of unbiased AI in personal devices, recognizing their potential impact on improving our health, lifestyle, and productivity -- more than any other technology.