# Datasheets for Machine Learning Sensors

arXiv: https://arxiv.org/abs/2306.08848 · HTML: https://mlsanity.com/paper/2306.08848/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 61 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [The Dataset Nutrition Label: A Framework To Drive Higher Data Quality Standards](https://mlsanity.com/paper/1805.03677/) (2018)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Event Based, Near Eye Gaze Tracking Beyond 10,000Hz](https://mlsanity.com/paper/2004.03577/) (2020)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Visual Wake Words Dataset](https://mlsanity.com/paper/1906.05721/) (2019)
- [The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence](https://mlsanity.com/paper/2201.03954/) (2022)
- [Conformity Assessments and Post-market Monitoring: A Guide to the Role of Auditing in the Proposed European AI Regulation](https://mlsanity.com/paper/2111.05071/) (2021)
- [Widening Access to Applied Machine Learning with TinyML](https://mlsanity.com/paper/2106.04008/) (2021)
- [Robustifying the Deployment of tinyML Models for Autonomous mini-vehicles](https://mlsanity.com/paper/2007.00302/) (2020)
- [Ask the Experts: What Should Be on an IoT Privacy and Security Label?](https://mlsanity.com/paper/2002.04631/) (2020)
- [ML-EXray: Visibility into ML Deployment on the Edge](https://mlsanity.com/paper/2111.04779/) (2021)
- [Machine Learning Sensors](https://mlsanity.com/paper/2206.03266/) (2022)
- [Is TinyML Sustainable? Assessing the Environmental Impacts of Machine Learning on Microcontrollers](https://mlsanity.com/paper/2301.11899/) (2023)
- [CamJ: Enabling System-Level Energy Modeling and Architectural Exploration for In-Sensor Visual Computing](https://mlsanity.com/paper/2304.03320/) (2023)

## Built on by (most cited first, 1 of 1)

- [TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems](https://mlsanity.com/paper/2411.07114/) (2024)

## Abstract (arXiv)

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 specifications, ML model and dataset characteristics, end-to-end performance metrics, and environmental impacts. Our framework addresses the continuous streaming nature of sensor data, real-time processing requirements, and embeds benchmarking methodologies that reflect real-world deployment conditions, ensuring practical viability. Aligned with the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), our approach enhances the transparency and reusability of ML sensor documentation across academic, industrial, and regulatory domains. To show the application of our approach, we present two datasheets: the first for an open-source ML sensor designed in-house and the second for a commercial ML sensor developed by industry collaborators, both performing computer vision-based person detection.