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Materiality and Risk in the Age of Pervasive AI Sensors

Mona Sloane, Emanuel Moss, Susan Kennedy, Matthew Stewart, Pete Warden, Brian Plancher, Vijay Janapa Reddi

TL;DR

The paper addresses AI risk in pervasive sensor-rich systems by foregrounding sensor materiality — the physical and social properties of sensing devices — and proposes a sensor-sensitive AI risk diagnostics framework. It analyzes the evolutionary trajectory of sensors from traditional devices to IoT, AIoT, Edge AI, and TinyML, and identifies three core calculative models driving proliferation: data-value pricing, data-stream monetization, and service-based monetization. The framework centers on five risk areas—calibration, documentation, proprietary data profusion, privacy, and waste—arguing that these capture material and economic drivers not fully addressed by existing RMF and EU AI Act frameworks. The authors advocate for a sensor design paradigm and broader stakeholder collaboration to enhance fairness, accountability, and transparency in sensor-based AI systems and to curb environmental and privacy harms. The work highlights governance gaps and offers concrete paths toward more responsible, community-centered sensor architectures and regulatory integration.

Abstract

Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable 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. Here we highlight the dimensions of risk associated with AI systems that arise from the material affordances of sensors and their underlying calculative models. We propose a sensor-sensitive framework for diagnosing these risks, complementing existing approaches such as the US National Institute of Standards and Technology AI Risk Management Framework and the European Union AI Act, and discuss its implementation. 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 sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency.

Materiality and Risk in the Age of Pervasive AI Sensors

TL;DR

The paper addresses AI risk in pervasive sensor-rich systems by foregrounding sensor materiality — the physical and social properties of sensing devices — and proposes a sensor-sensitive AI risk diagnostics framework. It analyzes the evolutionary trajectory of sensors from traditional devices to IoT, AIoT, Edge AI, and TinyML, and identifies three core calculative models driving proliferation: data-value pricing, data-stream monetization, and service-based monetization. The framework centers on five risk areas—calibration, documentation, proprietary data profusion, privacy, and waste—arguing that these capture material and economic drivers not fully addressed by existing RMF and EU AI Act frameworks. The authors advocate for a sensor design paradigm and broader stakeholder collaboration to enhance fairness, accountability, and transparency in sensor-based AI systems and to curb environmental and privacy harms. The work highlights governance gaps and offers concrete paths toward more responsible, community-centered sensor architectures and regulatory integration.

Abstract

Artificial intelligence (AI) systems connected to sensor-laden devices are becoming pervasive, which has notable 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. Here we highlight the dimensions of risk associated with AI systems that arise from the material affordances of sensors and their underlying calculative models. We propose a sensor-sensitive framework for diagnosing these risks, complementing existing approaches such as the US National Institute of Standards and Technology AI Risk Management Framework and the European Union AI Act, and discuss its implementation. 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 sensor design paradigm that empowers users and communities and leads to a future of increased fairness, accountability and transparency.
Paper Structure (15 sections, 1 figure)

This paper contains 15 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Sensors, Everywhere
  3. The 'Evolution' of Sensing
  4. Calculative Models and Sensor Development
  5. A Sensor-Sensitive AI Risk Diagnostics Framework
  6. Calibration
  7. Documentation
  8. Proprietary Data Profusion
  9. Privacy
  10. Waste
  11. Implementation
  12. Discussion and Future Work
  13. Conclusion
  14. Author Contributions
  15. Competing Interests Statement

Figures (1)

  • Figure 1: Timeline of sensor evolution: from passive analog detectors to intelligent IoT and ML-enabled systems. Examples of devices, their material affordances, and their calculative models are also included.