Materiality and Risk in the Age of Pervasive AI Sensors
CoRR(2024)
Abstract
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.
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