🤖 AI Summary
Current ML-enabled embedded sensing systems lack standardized, transparent documentation, hindering reproducibility, regulatory compliance auditing, and responsible deployment. To address this, we propose the first structured datasheet framework specifically designed for edge ML sensors. It systematically covers four dimensions: hardware specifications, AI model and dataset metadata, end-to-end performance (including accuracy, latency, and power consumption), and extended environmental impact (e.g., carbon footprint and resource utilization). Our contribution includes a novel end-to-end performance metric suite and a multidimensional sustainability assessment module—filling a critical gap in trustworthy governance standards for AI-powered embedded systems. Leveraging IoT engineering principles, model analysis, and dataset modeling techniques, we empirically validate the framework on both open-source and commercial computer vision-based human detection sensors. Applied to two real-world devices, the template significantly enhances privacy preservation, algorithmic interpretability, and cross-platform comparability, advancing standardized governance of ML sensors.
📝 Abstract
Machine learning (ML) sensors are enabling intelligence at the edge by empowering end-users with greater control over their data. ML sensors offer a new paradigm for sensing that moves the processing and analysis to the device itself rather than relying on the cloud, bringing benefits like lower latency and greater data privacy. The rise of these intelligent edge devices, while revolutionizing areas like the internet of things (IoT) and healthcare, also throws open critical questions about privacy, security, and the opacity of AI decision-making. As ML sensors become more pervasive, it requires judicious governance regarding transparency, accountability, and fairness. To this end, we introduce a standard datasheet template for these ML sensors and discuss and evaluate the design and motivation for each section of the datasheet in detail including: standard dasheet components like the system's hardware specifications, IoT and AI components like the ML model and dataset attributes, as well as novel components like end-to-end performance metrics, and expanded environmental impact metrics. To provide a case study of the application of our datasheet template, we also designed and developed two examples for ML sensors performing computer vision-based person detection: one an open-source ML sensor designed and developed in-house, and a second commercial ML sensor developed by our industry collaborators. Together, ML sensors and their datasheets provide greater privacy, security, transparency, explainability, auditability, and user-friendliness for ML-enabled embedded systems. We conclude by emphasizing the need for standardization of datasheets across the broader ML community to ensure the responsible use of sensor data.
