Datasheets for Machine Learning Sensors: Towards Transparency, Auditability, and Responsibility for Intelligent Sensing
CoRR(2023)
摘要
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.
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