Machine learning (ML) sensors offer a new paradigm for sensing that enables intelligence at the edge while empowering end-users with greater control of their data. As these ML sensors play a crucial role in the development of intelligent devices, clear documentation of their specifications, functionalities, and limitations is pivotal. This paper introduces a standard datasheet template for ML sensors and discusses its essential components including: the system's hardware, ML model and dataset attributes, end-to-end performance metrics, and environmental impact. We provide an example datasheet for our own ML sensor and discuss each section in detail. We highlight how these datasheets can facilitate better understanding and utilization of sensor data in ML applications, and we provide objective measures upon which system performance can be evaluated and compared. 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 and effective use of sensor data.

翻译：机器学习（ML）传感器提出了一种新的传感范式，可在边缘端实现智能处理，同时赋予终端用户对数据的更强控制权。随着这类ML传感器在智能设备开发中扮演关键角色，清晰记录其规格、功能和局限性变得至关重要。本文提出了一套适用于ML传感器的标准化数据表模板，并讨论了其核心组成部分，包括：系统硬件、ML模型与数据集属性、端到端性能指标以及环境影响。我们以自研ML传感器为例提供了一份完整的数据表，逐节详细阐述各模块内容，论证了这些数据表如何促进对ML应用中传感器数据的理解与利用，并提供了可客观评估和比较系统性能的指标。ML传感器及其数据表共同为基于ML的嵌入式系统提供了更强的隐私保护、安全性、透明度、可解释性、可审计性和用户友好性。最后我们强调，需要在更广泛的ML社区推进数据表标准化，以确保传感器数据得到负责任且有效的利用。