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.
This is a preview of subscription content, access via your institution
Access options
Access through your institution
Change institution
Buy or subscribe
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Learn more
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Learn more
Buy this article
Purchase on SpringerLink
Instant access to full article PDF
Buy now
Prices may be subject to local taxes which are calculated during checkout
Additional access options:
Log in
Learn about institutional subscriptions
Read our FAQs
Contact customer support
Fig. 1: Timeline of sensor evolution from passive analogue detectors to intelligent IoT and machine learning-enabled systems.
References
Metcalf, J., Moss, E., Watkins, E. A., Singh, R. & Elish, M. C. Algorithmic impact assessments and accountability: the co-construction of impacts. In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 735–746 (ACM, 2021).
Shelby, R. et al. Sociotechnical harms of algorithmic systems: scoping a taxonomy for harm reduction. In Proc. 2023 AAAI/ACM Conference on AI, Ethics, and Society 723–741 (ACM, 2023).
Weidinger, L. et al. Ethical and social risks of harm from language models. Preprint at https://arxiv.org/abs/2112.04359 (2021).
Selbst, A. D. An institutional view of algorithmic impact. Harv. J. Law Technol. 35, 117 (2021).
MATHGoogle Scholar
Birhane, A. Algorithmic injustice: a relational ethics approach. Patternshttps://doi.org/10.1016/j.patter.2021.100205 (2021).
Barocas, S. & Selbst, A. D. Big data’s disparate impact. Calif. Law Rev. 104, 671–732 (2016).
Google Scholar
Noble, S. U. Algorithms of Oppression: Data Discrimination in the Age of Google (New York Univ. Press, 2018).
Eubanks, V. Automating Inequality: How High-tech Tools Profile, Police, and Punish the Poor (St. Martin’s Press, 2018).
Danks, D. & London, A. J. Algorithmic bias in autonomous systems. In Proc. 26th International Joint Conference on Artificial Intelligence Vol. 17, 4691–4697 (AAAI, 2017).
Mitchell, S., Potash, E., Barocas, S., D’Amour, A. & Lum, K. Algorithmic fairness: choices, assumptions, and definitions. Annu. Rev. Stat. Appl. 8, 141–163 (2021).
ArticleMathSciNetGoogle Scholar
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. & Galstyan, A. A survey on bias and fairness in machine learning. ACM Comput. Surv. 54, 1–35 (2021).
ArticleMATHGoogle Scholar
Hazirbas, C. et al. Towards measuring fairness in AI: the casual conversations dataset. IEEE Trans. Biom. Behav. Identity Sci. 4, 324–332 (2021).
ArticleMATHGoogle Scholar
Ehsan, U., Singh, R., Metcalf, J. & Riedl, M. The algorithmic imprint. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 1305–1317 (ACM, 2022).
Dodge, J. et al. Measuring the carbon intensity of AI in cloud instances. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 1877–1894 (ACM, 2022).
Bender, E. M., Gebru, T., McMillan-Major, A &, Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (ACM, 2021).
Weidinger, L. et al. Taxonomy of risks posed by language models. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 214–229 (ACM, 2022).
Bridges, L. et al. Geographies of digital wasting: electronic waste from mine to discard and back again; https://www.geographiesofdigitalwasting.com/
Kidd, M. Energy and e-waste: the AI tsunamis. DCDhttps://www.datacenterdynamics.com/en/opinions/energy-and-e-waste-the-ai-tsunamis/ (2023).
Law, J. & Mol, A. Notes on materiality and sociality. Sociol. Rev. 43, 274–294 (1995).
ArticleMATHGoogle Scholar
Pinch, T. Technology and instituions: living in a material world. Theory Soc. 37, 461–483 (2008).
ArticleMATHGoogle Scholar
Lievrouw, L. A. & Livingstone, S. in Handbook of New Media: Social Shaping and Social Consequences of ICTs 1–14 (2006).
Miller, D. Materiality (Duke Univ. Press, (2020).
Warden, P. & Situnayake, D. TinyML: Machine Learning with Tensorflow Lite on Arduino and Ultra-low-power Microcontrollers (O’Reilly Media, 2019).
Janapa Reddi, V. et al. Widening access to applied machine learning with tinyML. Harv. Data Sci. Rev.https://doi.org/10.1162/99608f92.762d171a (2022).
Gillespie, T., Boczkowski, P. J. & Foot, K. A. in Media Technologies: Essays on Communication, Materiality, and Society 21–51 (MIT Press, 2013)
Vallor, S. Technology and the Virtues: A Philosophical Guide to A Future Worth Wanting (Oxford Univ. Press, 2016).
Artificial Intelligence Risk Management Framework (AI RMF 1.0) (National Institute of Standards and Technology, 2023); http://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf
Veale, M. & Zuiderveen Borgesius, F. Demystifying the draft EU Artificial Intelligence Act—analysing the good, the bad, and the unclear elements of the proposed approach. Comput. Law Rev. Int. 22, 97–112 (2021).
ArticleMATHGoogle Scholar
Mukhopadhyay, S. C. et al. Artificial intelligence-based sensors for next generation IoT applications: a review. IEEE Sens. J. 21, 24920–24932 (2021).
ArticleMATHGoogle Scholar
Singh, R. & Gill, S. S. Edge AI: a survey. Internet Things Cyber Phys. Syst. 3, 71–92 (2023).
ArticleMATHGoogle Scholar
Haick, H. & Tang, N. Artificial intelligence in medical sensors for clinical decisions. ACS Nano 15, 3557–3567 (2021).
ArticleMATHGoogle Scholar
Zhang, L. & Zhang, L. Artificial intelligence for remote sensing data analysis: a review of challenges and opportunities. IEEE Geosc. Remote Sens. Mag. 10, 270–294 (2022).
ArticleMATHGoogle Scholar
Masson, J. F. Roadmap for the Use of Machine Learning and Artificial Intelligence in Sensing (ACS, 2024).
Ullo, S. L. & Sinha, G. R. Advances in IoT and smart sensors for remote sensing and agriculture applications. Remote Sens. 13, 2585 (2021).
ArticleMATHGoogle Scholar
Merenda, M., Porcaro, C. & Iero, D. Edge machine learning for AI-enabled IoT devices: a review. Sensors 20, 2533 (2020).
ArticleGoogle Scholar
Zhu, G. et al. Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G. Sci. China Inf. Sci. 66, 130301 (2023).
ArticleMATHGoogle Scholar
Abadade, Y. et al. A comprehensive survey on TinyML. IEEE Access 11, 96892–96922 (2023).
ArticleGoogle Scholar
Dutta, L. & Bharali, S. TinyML meets IoT: a comprehensive survey. Internet Things 16, 100461 (2021).
ArticleMATHGoogle Scholar
Gaver, W. W. Technology affordances. In Proc. SIGCHI Conference on Human Factors in Computing Systems 79–84 (ACM, 1991).
Gibson, J. In Perceiving, Acting and Knowing: Toward an Ecological Psychology (eds Shaw, R. & Bransford, J.) 1st edn (1977).
Davis, J. L. How Artifacts Afford: the Power and Politics of Everyday Things (MIT Press, 2020).
Kennewell, S. Using affordances and constraints to evaluate the use of information and communications technology in teaching and learning. J. Inf. Techol. Teacher Educ. 10, 101–116 (2001).
Google Scholar
Acemoglu, D. Harms of AI (National Bureau of Economic Research, 2021).
Kusche, I. Possible harms of artificial intelligence and the EU AI Act: fundamental rights and risk. J. Risk Res.https://doi.org/10.1080/13669877.2024.2350720 (2024).
Watkins, E. A., Moss, E., Metcalf, J., Singh, R. & Elish, M. C. Governing algorithmic systems with impact assessments: six observations. In Proc. 2021 AAAI/ACM Conference on AI, Ethics, and Society 1010–1022 (ACM, 2021).
Bengio, Y. et al. Managing extreme AI risks amid rapid progress. Science 384, 842–845 (2024).
ArticleMATHGoogle Scholar
Roth, L. Looking at Shirley, the ultimate norm: colour balance, image technologies, and cognitive equity. Can. J. Commun. 34, 111–136 (2009).
ArticleMATHGoogle Scholar
Galdino, G. M., Vogel, J. E. & Vander Kolk, C. A. Standardizing digital photography: it’s not all in the eye of the beholder. Plas. Reconstr. Surg. 108, 1334–1344 (2001).
ArticleGoogle Scholar
Guo, C. Y., Huang, W. Y., Chang, H. C. & Hsieh, T. L. Calibrating oxygen saturation measurements for different skin colors using the individual typology angle. IEEE Sens. J. 23, 16993–17001 (2023).
Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency 77–91 (PMLR, 2018).
Muniesa, F., Millo, Y. & Callon, M. An introduction to market devices. Sociol. Rev. 55, 1–12 (2007).
ArticleGoogle Scholar
Callon, M. & Law, J. On qualculation, agency, and otherness. Environ. Plan. D 23, 717–733 (2005).
ArticleMATHGoogle Scholar
IoT device detects wind turbine faults in the field. https://www.engineering.com/iot-device-detects-wind-turbine-faults-in-the-field/ (2020).
Restle, P. J. et al. The clock distribution of the power4 microprocessor. In 2002 IEEE International Solid-State Circuits Conference. Digest of Technical Papers Vol. 1, 144–145 (IEEE, 2002).
Flores, T. et al. TinyML for safe driving: the use of embedded machine learning for detecting driver distraction. In 2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive) 62–66 (IEEE, 2023).
Shah, A. S., Nasir, H., Fayaz, M., Lajis, A. & Shah, A. A review on energy consumption optimization techniques in IoT based smart building environments. Information 10, 108 (2019).
ArticleMATHGoogle Scholar
Transforming our World: The 2030 Agenda for Sustainable Development (United Nations, 2015); https://sdgs.un.org/publications/transforming-our-world-2030-agenda-sustainable-development-17981
Prakash, S. et al. Is tinyML sustainable? Assessing the environmental impacts of machine learning on microcontrollers. Commun. ACM 66, 68–77 (2023).
ArticleMATHGoogle Scholar
Mrisho, L. M. et al. Accuracy of a smartphone-based object detection model, PlantVillage Nuru, in identifying the foliar symptoms of the viral diseases of cassava-CMD and CBSD. Front. Plant Sci. 11, 590889 (2020).
ArticleGoogle Scholar
King, A. Technology: the future of agriculture. Nature. 544, S21–S23 (2017).
ArticleMATHGoogle Scholar
Solana, A. Elephants vs trains: this is how AI helps ensure they don’t collide. ZDNEThttps://www.zdnet.com/article/elephants-vs-trains-this-is-how-ai-helps-ensure-they-dont-collide/ (2020).
Temple-Raston, D. Using AI in Malawi to save elephants. NPRhttps://www.npr.org/2019/09/17/761682912/using-ai-in-malawi-to-save-elephants (2019).
Johnson, K. Google’s AI powers real-time orca tracking in Vancouver Bay. VentureBeathttps://venturebeat.com/ai/googles-ai-powers-real-time-orca-tracking-in-vancouver-bay/ (2020).
Elmqvist, N. Data analytics anywhere and everywhere. Commun. ACM 66, 52–63 (2023).
ArticleMATHGoogle Scholar
Zuboff, S. The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power 1st edn (PublicAffairs, 2018).
Fernback, J. Sousveillance: communities of resistance to the surveillance environment. Telemat. Inform. 30, 11–21 (2013).
ArticleGoogle Scholar
Monahan, T. Regulating belonging: surveillance, inequality, and the cultural production of abjection. J. Cult. Econ. 10, 191–206 (2017).
ArticleMATHGoogle Scholar
Sevignani, S. Surveillance, classification, and social inequality in informational capitalism: the relevance of exploitation in the context of markets in information. Hist. Soc. Res. 42, 77–102 (2017).
Google Scholar
Gilman, M. & Green, R. The surveillance gap: the harms of extreme privacy and data marginalization. NYU Rev. Law Soc. Change 42, 253 (2018).
MATHGoogle Scholar
Parsons, C. Beyond privacy: articulating the broader harms of pervasive mass surveillance. Media Commun. 3, 1–11 (2015).
ArticleMATHGoogle Scholar
AI Act (European Commission, 2025); https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
Wilson, J. S. Sensor Technology Handbook (Elsevier, 2004).
Hacking, I. In Biopower: Foucault and Beyond (eds Cisney, V. W. & Morar, N.) 65–81 (Univ. Chicago Press, 2015).
Krizhevsky, A., Sutskever, I. & Hinton, G.E. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, (2012).
Ardila, R. et al. Common voice: a massively-multilingual speech corpus. Preprint at https://arxiv.org/abs/1912.06670 (2019).
Cowan, R. S. in The Social Shaping of Technology: How the Refrigerator Got its Hum (eds MacKenzie, D. A. & Wajcman, J.) 202–218 (Open Univ. Press, 1985).
Gibson, J. J. in The People, Place, and Space Reader (eds Gieseking, J. J. et al.) 56–60 (Routledge, 2014).
Buratti, C., Conti, A., Dardari, D. & Verdone, R. An overview on wireless sensor networks technology and evolution. Sensors 9, 6869–6896 (2009).
ArticleMATHGoogle Scholar
Mainetti, L., Patrono, L. & Vilei, A. Evolution of wireless sensor networks towards the internet of things: a survey. In SoftCOM 2011, 19th International Conference on Software, Telecommunications and Computer Networks 1–6 (IEEE, 2011).
Warden, P., Stewart, M., Plancher, B., Katti, S. & Reddi, V. J. Machine learning sensors: a design paradigm for the future of intelligent sensors. Commun. ACM 66, 25–28 (2023).
ArticleGoogle Scholar
Zhang, Y., Gu, Y., Vlatkovic, V. & Wang, X. Progress of smart sensor and smart sensor networks. In Fifth World Congress on Intelligent Control and Automation Vol. 4, 3600–3606 (IEEE, 2004).
Vetelino, J. & Reghu, A. Introduction to Sensors (CRC Press, 2017).
Soloman, S. Sensors Handbook (McGraw-Hill, 2009).
Li, S., Xu, L. D. & Zhao, S. The Internet of Things: a survey. Inf. Syst. Front. 17, 243–259 (2015).
ArticleMATHGoogle Scholar
Rose, K., Eldridge, S. & Chapin, L. The Internet of Things: An Overview (The Internet Society, 2015).
Sehrawat, D. & Gill, N. S. Smart sensors: analysis of different types of IoT sensors. In 2019 3rd International Conference on Trends in Electronics and Informatics 523–528 (IEEE, 2019).
Krishnamurthi, R., Kumar, A., Gopinathan, D., Nayyar, A. & Qureshi, B. An overview of IoT sensor data processing, fusion, and analysis techniques. Sensors 20, 6076 (2020).
ArticleGoogle Scholar
Kocakulak, M. & Butun, I. An overview of wireless sensor networks towards Internet of Things. In 2017 IEEE 7th Annual Computing and Communication Workshop and Conference 1–6 (IEEE, 2017).
Vincent, D. R. et al. Sensors driven AI-based agriculture recommendation model for assessing land suitability. Sensors 19, 3667 (2019).
ArticleMATHGoogle Scholar
Fabre, W., Haroun, K., Lorrain, V., Lepecq, M. & Sicard, G. From near-sensor to in-sensor: a state-of-the-art review of embedded AI vision systems. Sensors 24, 5446 (2024).
ArticleGoogle Scholar
Wen, D. et al. Task-oriented sensing, computation, and communication integration for multi-device edge AI. IEEE Trans. Wirel. Commun. 23, 2486–2502 (2023).
ArticleMATHGoogle Scholar
Sodhro, A. H., Pirbhulal, S. & De Albuquerque, V. H. C. Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Trans. Industr. Inform. 15, 4235–4243 (2019).
ArticleMATHGoogle Scholar
Stewart, M. et al. Datasheets for machine learning sensors: towards transparency, auditability, and responsibility for intelligent sensing. Preprint at https://doi.org/10.48550/arXiv.2306.08848 (2024).
Callon, M. & Muniesa, F. Peripheral vision: economic markets as calculative collective devices. Organ. Stud. 26, 1229–1250 (2005).
ArticleGoogle Scholar
Hansen, K. B. Model talk: calculative cultures in quantitative finance. Sci. Technol. Hum. Values 46, 600–627 (2021).
ArticleMATHGoogle Scholar
Besedovsky, N. Financialization as calculative practice: the rise of structured finance and the cultural and calculative transformation of credit rating agencies. Socioecon. Rev. 16, 61–84 (2018).
ArticleGoogle Scholar
MacKenzie, D. An Engine, Not a Camera: How Financial Models Shape Markets 1st edn (MIT Press, 2008).
2019 Manufacturing Trends Report (Microsoft Dynamics 365, 2019); https://info.microsoft.com/rs/157-GQE-382/images/EN-US-CNTNT-Report-2019-Manufacturing-Trends.pdf
Huck, C. W. In Sense the Real Change: Proc. 20th International Conference on Near Infrared Spectroscopy (eds Chu, X. et al.) 59–72 (Springer Nature, 2022).
Roy, R. & Miller, J. Miniaturization of image sensors: the role of innovations in complementary technologies in overcoming technological trade-offs associated with product innovation. J. Eng. Technol. Manag. 44, 58–69 (2017).
ArticleGoogle Scholar
Rodriguez-Saona, L., Aykas, D. P., Borba, K. R. & Urtubia, A. Miniaturization of optical sensors and their potential for high-throughput screening of foods. Curr. Opin. Food Sci. 31, 136–150 (2020).
ArticleGoogle Scholar
Tricoli, A., Nasiri, N. & De, S. Wearable and miniaturized sensor technologies for personalized and preventive medicine. Adv. Funct. Mater. 27, 1605271 (2017).
ArticleMATHGoogle Scholar
Frazier, A. B., Warrington, R. O. & Friedrich, C. The miniaturization technologies: past, present, and future. IEEE Trans. Industr. Electron. 42, 423–430 (1995).
ArticleMATHGoogle Scholar
Madou, M. J. Fundamentals of Microfabrication: The Science of Miniaturization (CRC Press, 2018).
Yang, Z., Albrow-Owen, T., Cai, W. & Hasan, T. Miniaturization of optical spectrometers. Science 371, eabe0722 (2021).
Jiang, C. et al. Energy aware edge computing: a survey. Comput. Commun. 151, 556–580 (2020).
ArticleMATHGoogle Scholar
Chen, Y. et al. Energy efficient dynamic offloading in mobile edge computing for Internet of Things. IEEE Trans. Cloud Comput. 9, 1050–1060 (2019).
ArticleMATHGoogle Scholar
Rault, T., Bouabdallah, A. & Challal, Y. Energy efficiency in wireless sensor networks: a top-down survey. Comput. Netw. 67, 104–122 (2014).
ArticleMATHGoogle Scholar
Sun, H. et al. MEMS based energy harvesting for the Internet of Things: a survey. Microsyst. Technol. 24, 2853–2869 (2018).
ArticleMATHGoogle Scholar
Raha, A. & Raghunathan, V. Towards full-system energy–accuracy tradeoffs: a case study of an approximate smart camera system. In Proc. 54th Annual Design Automation Conference 2017 1–6 (ACM, 2017).
Schurgers, C. & Srivastava, M. B. Energy efficient routing in wireless sensor networks. In 2001 MILCOM Proceedings Communications for Network-centric Operations: Creating the Information Force Vol. 1, 357–361 (IEEE, 2001).
State of IoT—Spring 2023 (IoT Analytics, 2023); https://iot-analytics.com/product/state-of-iot-spring-2023/
Saif, I. & Ammanath, B. ‘Trustworthy AI’ is a framework to help manage unique risk. MIT Technology Reviewhttps://www.technologyreview.com/2020/03/25/950291/trustworthy-ai-is-a-framework-to-help-manage-unique-risk/ (2020).
Floridi, L. et al. capAI: a procedure for conducting conformity assessment of AI systems in line with the EU Artificial Intelligene Act. Preprint at SSRNhttps://doi.org/10.2139/ssrn.4064091 (2022).
Advancing Accountability in AI: Governing and Managing Risks throughout the Lifecycle for Trustworthy AI OECD Digital Economy Papers Vol. 349 (OECD, 2023); https://www.oecd-ilibrary.org/science-and-technology/advancing-accountability-in-ai_2448f04b-en
Baquero, J. A., Burkhardt, R., Govindarajan, A. & Wallace, T. Derisking AI: Risk Management in AI Development (McKinsey, 2020).
AI and Risk Management (Deloitte, 2018); https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/innovatie/deloitte-nl-innovate-lu-ai-and-risk-management.pdf
Whitehouse, K. & Culler, D. Calibration as parameter estimation in sensor networks. In Proc. 1st ACM International Workshop on Wireless Sensor Networks and Applications 59–67 (ACM, 2002).
Hansen, J. H. & Boril, H. Gunshot detection systems: methods, challenges, and can they be trusted? In Audio Engineering Society Convention Convention Paper 10540 (AES, 2021).
Delaine, F., Lebental, B. & Rivano, H. In situ calibration algorithms for environmental sensor networks: a review. IEEE Sens. J. 19, 5968–5978 (2019).
ArticleMATHGoogle Scholar
Leveson, N. G. Engineering a Safer World: Systems Thinking Applied to Safety (MIT Press, 2016).
Dewey, F. R. A complete guide to data sheets. Sensors Magazine (1998); https://www.allegromicro.com/-/media/files/technical-documents/complete-guide-to-datasheets-pub26000.pdf
Mitchell, M. et al. Model cards for model reporting. In Proc. Conference on Fairness, Accountability, and Transparency 220–229 (ACM, 2019).
Mitev, R., Pazii, A., Miettinen, M., Enck, W. & Sadeghi, A. R. Leakypick: IoT audio spy detector. In Proc. 36th Annual Computer Security Applications Conference 694–705 (ACM, 2020).
Anthes, G. Data brokers are watching you. Commun. ACM 58, 28–30 (2015).
ArticleGoogle Scholar
Moyopo, S. Quantifying the data currency’s impact on the profit made by data brokers in the Internet of Things based data marketplace. Eur. J. Electr. Eng. Comput. Sci. 7, 7–16 (2023)
Crain, M. The limits of transparency: data brokers and commodification. New Media Soc. 20, 88–104 (2018).
ArticleMATHGoogle Scholar
Teh, H. Y., Kempa-Liehr, A. W. & Wang, K. I. K. Sensor data quality: a systematic review. J. Big Data 7, 11 (2020).
Ye, J., Stevenson, G. & Dobson, S. Detecting abnormal events on binary sensors in smart home environments. Pervasive Mob. Comput. 33, 32–49 (2016).
ArticleMATHGoogle Scholar
D’ignazio, C. & Klein, L. F. Data Feminism (MIT Press, 2023).
O’Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2017).
Crawford, K. The hidden biases in big data. Harvard Business Review (2013); https://hbr.org/2013/04/the-hidden-biases-in-big-data
Ridzuan, F. & Zainon, W. M. N. W. A review on data cleansing methods for big data. Procedia Comput. Sci. 161, 731–738 (2019).
ArticleGoogle Scholar
Scott, E. The Trouble with Informed Consent in Smart Cities (IAAP, 2019).
Froomkin, A. M. Big data: destroyer of informed consent. Yale J. Law Technol. 21, 27 (2019).
Google Scholar
Elmenreich, W. An Introduction to Sensor Fusion (Vienna Univ. Technology, 2002).
Sweeney, L. Simple demographics often identify people uniquely. Health 671, 1–34 (2000).
Google Scholar
Barocas, S. & Nissenbaum, H. Big data’s end run around procedural privacy protections. Commun. ACM 57, 31–33 (2014).
ArticleGoogle Scholar
Ding, W., Jing, X., Yan, Z. & Yang, L. T. A survey on data fusion in Internet of Things: towards secure and privacy-preserving fusion. Inf. Fusion 51, 129–144 (2019).
ArticleMATHGoogle Scholar
Dhar, P. The carbon impact of artificial intelligence. Nat. Mach. Intell. 2, 423–425 (2020).
ArticleMATHGoogle Scholar
Wu, C. J. et al. Sustainable AI: environmental implications, challenges and opportunities. Proc. Mach. Learn. Syst. 4, 795–813 (2022).
MATHGoogle Scholar
Van Wynsberghe, A. Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics 1, 213–218 (2021).
ArticleMATHGoogle Scholar
Lannelongue, L., Grealey, J. & Inouye, M. Green algorithms: quantifying the carbon footprint of computation. Adv. Sci. 8, 2100707 (2021).
ArticleGoogle Scholar
Cooper, Z. G. T. Of dog kennels, magnets, and hard drives: dealing with big data peripheries. Big Data Soc. 8, 20539517211015430 (2021).
ArticleGoogle Scholar
Bridges, L. E. Material entanglements of community surveillance & infrastructural power. AoIR Selected Papers of Internet Research, 2020https://doi.org/10.5210/spir.v2020i0.11179 (2020).
Gupta, U. et al. Chasing carbon: the elusive environmental footprint of computing. In 2021 IEEE International Symposium on High-Performance Computer Architecture 854–867 (IEEE, 2021).
Ozer, E. et al. Bendable non-silicon RISC-V microprocessor. Nature 634, 341–346 (2024).
ArticleMATHGoogle Scholar
Sorrell, S. Jevons’ paradox revisited: the evidence for backfire from improved energy efficiency. Energy Policy 37, 1456–1469 (2009).
ArticleMATHGoogle Scholar
Verbeek, P. P. Ambient intelligence and persuasive technology: the blurring boundaries between human and technology. Nanoethics. 3, 231–242 (2009).
ArticleMATHGoogle Scholar
Nafus, D. Quantified: Biosensing Technologies in Everyday Life (MIT Press, 2016).
Wang, X., McGill, T. J. & Klobas, J. E. I want it anyway: consumer perceptions of smart home devices. J. Comput. Inf. Syst. 60, 1–11 (2018).
Balta-Ozkan, N., Davidson, R., Bicket, M. & Whitmarsh, L. Social barriers to the adoption of smart homes. Energy Policy 63, 363–374 (2013).
ArticleGoogle Scholar
Joint Task Force Transformation Initiative Risk Management Framework for Information Systems and Organizations: A System Life Cycle Approach for Security and Privacy NIST SP 800-37r2 (National Institute of Standards and Technology, 2018); https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-37r2.pdf
Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).
ArticleMATHGoogle Scholar
Soltoggio, A. et al. A collective AI via lifelong learning and sharing at the edge. Nat. Mach. Intell. 6, 251–264 (2024).
ArticleMATHGoogle Scholar
Algorithmic Impact Assessment: A Case Study in Healthcare (Ada Lovelace Institute, 2022); https://www.adalovelaceinstitute.org/report/algorithmic-impactasssessment-case-study-healthcare
Metcalf, J. et al. A relationship and not a thing: a relational approach to algorithmic accountability and assessment documentation. Preprint at https://arxiv.org/abs/2203.01455 (2022).
Lavin, A. et al. Technology readiness levels for machine learning systems. Nat. Commun. 13, 6039 (2022).
ArticleMATHGoogle Scholar
Huckelberry, J. et al. TinyML security: exploring vulnerabilities in resource-constrained machine learning systems. Preprint https://arxiv.org/abs/2411.07114 (2024).
Baxter, G. & Sommerville, I. Socio-technical systems: from design methods to systems engineering. Interact. Comput. 23, 4–17 (2011).
ArticleMATHGoogle Scholar
Bauer, J. M. & Herder, P. M. in Philosophy of Technology and Engineering Sciences (ed. Meijers, A.) 601–630 (Elsevier, 2009).
Leslie, D. Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. Zenodohttps://doi.org/10.5281/zenodo.3240528 (2019).
Abbas, R., Pitt, J. & Michael, K. Socio-technical design for public interest technology. IEEE Trans. Technol. Soc. 2, 55–61 (2021).
ArticleMATHGoogle Scholar
Plancher, B. et al. TinyML4D: scaling embedded machine learning education in the developing world. In Proc. AAAI Symposium Series Vol. 3, 508–515 (AAAI, 2024).
Download references
Author information
Authors and Affiliations
University of Virginia, Charlottesville, VA, USA
Mona Sloane
Intel Labs, Hillsboro, OR, USA
Emanuel Moss
Santa Clara University, Santa Clara, CA, USA
Susan Kennedy
Harvard University, Boston, MA, USA
Matthew Stewart & Vijay Janapa Reddi
Stanford University, Stanford, CA, USA
Pete Warden
Barnard College, Columbia University, New York, NY, USA
Brian Plancher
Authors
Mona Sloane
View author publications
You can also search for this author inPubMedGoogle Scholar
2. Emanuel Moss
View author publications
You can also search for this author inPubMedGoogle Scholar
3. Susan Kennedy
View author publications
You can also search for this author inPubMedGoogle Scholar
4. Matthew Stewart
View author publications
You can also search for this author inPubMedGoogle Scholar
5. Pete Warden
View author publications
You can also search for this author inPubMedGoogle Scholar
6. Brian Plancher
View author publications
You can also search for this author inPubMedGoogle Scholar
7. Vijay Janapa Reddi
View author publications
You can also search for this author inPubMedGoogle Scholar
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.
Ethics declarations
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.
Peer review
Peer review information
Nature Machine Intelligence thanks Carina Prunkl and Andrea Soltoggio for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
About this article
Check for updates. Verify currency and authenticity via CrossMark
Cite this article
Sloane, M., Moss, E., Kennedy, S. et al. Materiality and risk in the age of pervasive AI sensors. Nat Mach Intell (2025). https://doi.org/10.1038/s42256-025-01017-7
Download citation
Received:26 April 2024
Accepted:28 January 2025
Published:20 March 2025
DOI:https://doi.org/10.1038/s42256-025-01017-7
Share this article
Anyone you share the following link with will be able to read this content:
Get shareable link
Sorry, a shareable link is not currently available for this article.
Copy to clipboard
Provided by the Springer Nature SharedIt content-sharing initiative