Datasheets for Machine Learning Sensors
Matthew Stewart, Yuke Zhang, Pete Warden, Yasmine Omri, Shvetank Prakash, Jacob Huckelberry, Joao Henrique Santos, Shawn Hymel, Benjamin Yeager Brown, Jim MacArthur, Nat Jeffries, Emanuel Moss, Mona Sloane, Brian Plancher, and Vijay Janapa Reddi

TL;DR
This paper introduces a comprehensive datasheet framework for ML sensors, enhancing transparency, reproducibility, and regulatory compliance of embedded AI sensing systems through detailed documentation templates.
Contribution
It provides a collaboratively developed datasheet template capturing hardware, ML models, datasets, performance, and environmental impacts for ML sensors, addressing real-time data and benchmarking needs.
Findings
Applied to open-source and commercial ML sensors for person detection.
Enhanced transparency and reusability of ML sensor documentation.
Facilitated compliance with regulatory and industry standards.
Abstract
Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These "ML sensors" enable context-sensitive, real-time data collection and decision-making across diverse applications ranging from anomaly detection in industrial settings to wildlife tracking for conservation efforts. As such, there is a need to provide transparency in the operation of such ML-enabled sensing systems through comprehensive documentation. This is needed to enable their reproducibility, to address new compliance and auditing regimes mandated in regulation and industry-specific policy, and to verify and validate the responsible nature of their operation. To address this gap, we introduce the datasheet for ML sensors framework. We provide a comprehensive template, collaboratively developed in academia-industry partnerships, that captures the distinct attributes of ML sensors, including hardware…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIoT and Edge/Fog Computing · Mobile Crowdsensing and Crowdsourcing · Context-Aware Activity Recognition Systems
