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Materiality and risk in the age of pervasive AI sensors

Nature Machine Intelligence volume 7, pages 334–345 (2025)Cite this article

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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.

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Fig. 1: Timeline of sensor evolution from passive analogue detectors to intelligent IoT and machine learning-enabled systems.

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Authors and Affiliations

  1. University of Virginia, Charlottesville, VA, USA

    Mona Sloane

  2. Intel Labs, Hillsboro, OR, USA

    Emanuel Moss

  3. Santa Clara University, Santa Clara, CA, USA

    Susan Kennedy

  4. Harvard University, Boston, MA, USA

    Matthew Stewart & Vijay Janapa Reddi

  5. Stanford University, Stanford, CA, USA

    Pete Warden

  6. Barnard College, Columbia University, New York, NY, USA

    Brian Plancher

Authors
  1. Mona Sloane
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  2. Emanuel Moss
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  3. Susan Kennedy
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  4. Matthew Stewart
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  5. Pete Warden
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  6. Brian Plancher
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  7. Vijay Janapa Reddi
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Contributions

M. Stewart and V.J.R. organized the exploratory seminar that led to this paper. All authors developed the problem statement. M. Sloane developed the theoretical approach. M. Stewart, E.M., S.K., B.P., M.P.S. and V.J.R. designed and executed the analytical approach. S.K., M. Sloane and E.M. conducted the policy analysis. B.P., M. Stewart, V.J.R. and P.W. produced the technical framework and historical analysis. M. Sloane, E.M., S.K., M. Stewart, B.P. and V.J.R. wrote the paper with input from all authors. B.P. managed the layout, formatting and figure design. M. Sloane managed the authorship, submission and revision process.

Corresponding author

Correspondence to Mona Sloane.

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Competing interests

P.W. is a founder and major shareholder of Useful Sensors Inc., which works on privacy-preserving sensor technology. The other authors declare no competing interests.

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Nature Machine Intelligence thanks Carina Prunkl and Andrea Soltoggio for their contribution to the peer review of this work.

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Sloane, M., Moss, E., Kennedy, S. et al. Materiality and risk in the age of pervasive AI sensors. Nat Mach Intell 7, 334–345 (2025). https://doi.org/10.1038/s42256-025-01017-7

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