Materiality and Risk in the Age of Pervasive AI Sensors
<|reference_start|>Materiality and Risk in the Age of Pervasive AI Sensors: Artificial intelligence systems connected to sensor-laden devices are becoming pervasive, which has significant implications for a range of AI risks, including to privacy, the environment, autonomy, and more. There is therefore a growing need for increased accountability around the responsible development and deployment of these technologies. In this paper, we provide a comprehensive analysis of the evolution of sensors, the risks they pose by virtue of their material existence in the world, and the impacts of ubiquitous sensing and on-device AI. We propose incorporating sensors into risk management frameworks and call for more responsible sensor and system design paradigms that address risks of such systems. To do so, we trace the evolution of sensors from analog devices to intelligent, networked systems capable of real-time data analysis and decision-making at the extreme edge of the network. We show that the proliferation of sensors is driven by calculative models that prioritize data collection and cost reduction and produce risks that emerge around privacy, surveillance, waste, and power dynamics. We then analyze these risks, highlighting issues of validity, safety, security, accountability, interpretability, and bias. We surface sensor-related risks not commonly captured in existing approaches to AI risk management, using a materiality lens that reveals how physical sensor properties shape data and algorithmic models. We conclude by advocating for increased attention to the materiality of algorithmic systems, and of on-device AI sensors in particular, and highlight the need for development of a responsible sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency.<|reference_end|>
@article{stewart2024materiality,
    title={Materiality and Risk in the Age of Pervasive AI Sensors},
    author={Matthew Stewart, Emanuel Moss, Pete Warden, Brian Plancher, Susan
  Kennedy, Mona Sloane, Vijay Janapa Reddi},
    journal={arXiv preprint arXiv:2402.11183},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11183},
    primaryClass={cs.CY cs.HC}
}