Ethical Wearable Data Collection from Sanitation Workers: A Consent Framework for Robotics Training Corpora

Abstract

Training robotic systems to perform hazardous tasks currently done by human workers requires demonstration data from those workers. This creates an ethical tension: the workers whose labor generates the training data are the same population whose livelihoods may be affected by the resulting automation. We present a consent and compensation framework developed through two years of fieldwork with sanitation workers in Indian municipalities. The framework addresses four dimensions: informed consent in workers' native languages with visual documentation of data usage; fair compensation that exceeds daily wages and includes participation in downstream value; data sovereignty provisions that give workers and their cooperatives control over how their data is used; and transition support connecting workers to alternative employment. We report on the framework's implementation across three field campaigns involving 47 workers, document the challenges of operationalizing ethical data collection in informal labor contexts, and propose standards for the emerging field of human-to-robot knowledge transfer. The framework is published as an open protocol for other robotics research groups working in labor substitution contexts.

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