Materiality and risk in the age of pervasive AI sensors
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 (2021).
Selbst, A. D. An institutional view of algorithmic impact. Harv. J. law Technol. 35, 117 (2021).
Google Scholar
Birhane, A. Algorithmic injustice: a relational ethics approach. Patterns (2021).
Barocas, S. & Selbst, A. D. Big data’s disparate impact. Calif. Law Rev. 104, 671–732 (2016).
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).
Google 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).
Google Scholar
Hazirbas, C. et al. Towards measuring fairness in AI: the casual conversations dataset. IEEE Trans. Biom. Behav. Identity Sci. 4, 324–332 (2021).
Google 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; class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”18.”>
Kidd, M. Energy and e-waste: the AI tsunamis. DCD / (2023).
Law, J. & Mol, A. Notes on materiality and sociality. Sociol. Rev. 43, 274–294 (1995).
Google Scholar
Pinch, T. Technology and instituions: living in a material world. Theory Soc. 37, 461–483 (2008).
Google 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. (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); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”28.”>
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).
Google Scholar
Mukhopadhyay, S. C. et al. Artificial intelligence-based sensors for next generation IoT applications: a review. IEEE Sens. J. 21, 24920–24932 (2021).
Google Scholar
Singh, R. & Gill, S. S. Edge AI: a survey. Internet Things Cyber Phys. Syst. 3, 71–92 (2023).
Google Scholar
Haick, H. & Tang, N. Artificial intelligence in medical sensors for clinical decisions. ACS Nano 15, 3557–3567 (2021).
Google 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).
Google 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).
Google Scholar
Merenda, M., Porcaro, C. & Iero, D. Edge machine learning for AI-enabled IoT devices: a review. Sensors 20, 2533 (2020).
Google 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).
Google Scholar
Abadade, Y. et al. A comprehensive survey on TinyML. IEEE Access 11, 96892–96922 (2023).
Google Scholar
Dutta, L. & Bharali, S. TinyML meets IoT: a comprehensive survey. Internet Things 16, 100461 (2021).
Google 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).
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. (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).
Google Scholar
Roth, L. Looking at Shirley, the ultimate norm: colour balance, image technologies, and cognitive equity. Can. J. Commun. 34, 111–136 (2009).
Google 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).
Google 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).
Google Scholar
Callon, M. & Law, J. On qualculation, agency, and otherness. Environ. Plan. D 23, 717–733 (2005).
Google Scholar
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).
Google Scholar
Transforming our World: The 2030 Agenda for Sustainable Development (United Nations, 2015); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”58.”>
Prakash, S. et al. Is tinyML sustainable? Assessing the environmental impacts of machine learning on microcontrollers. Commun. ACM 66, 68–77 (2023).
Google 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).
Google Scholar
King, A. Technology: the future of agriculture. Nature. 544, S21–S23 (2017).
Google Scholar
Solana, A. Elephants vs trains: this is how AI helps ensure they don’t collide. ZDNET / (2020).
Temple-Raston, D. Using AI in Malawi to save elephants. NPR (2019).
Johnson, K. Google’s AI powers real-time orca tracking in Vancouver Bay. VentureBeat / (2020).
Elmqvist, N. Data analytics anywhere and everywhere. Commun. ACM 66, 52–63 (2023).
Google 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).
Google Scholar
Monahan, T. Regulating belonging: surveillance, inequality, and the cultural production of abjection. J. Cult. Econ. 10, 191–206 (2017).
Google 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).
Gilman, M. & Green, R. The surveillance gap: the harms of extreme privacy and data marginalization. NYU Rev. Law Soc. Change 42, 253 (2018).
Google Scholar
Parsons, C. Beyond privacy: articulating the broader harms of pervasive mass surveillance. Media Commun. 3, 1–11 (2015).
Google Scholar
AI Act (European Commission, 2025); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”72.”>
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 (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).
Google 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).
Google 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).
Google 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).
Google 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).
Google 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).
Google 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).
Google 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).
Google Scholar
Stewart, M. et al. Datasheets for machine learning sensors: towards transparency, auditability, and responsibility for intelligent sensing. Preprint at (2024).
Callon, M. & Muniesa, F. Peripheral vision: economic markets as calculative collective devices. Organ. Stud. 26, 1229–1250 (2005).
Google Scholar
Hansen, K. B. Model talk: calculative cultures in quantitative finance. Sci. Technol. Hum. Values 46, 600–627 (2021).
Google 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).
Google 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); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”99.”>
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).
Google 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).
Google Scholar
Tricoli, A., Nasiri, N. & De, S. Wearable and miniaturized sensor technologies for personalized and preventive medicine. Adv. Funct. Mater. 27, 1605271 (2017).
Google Scholar
Frazier, A. B., Warrington, R. O. & Friedrich, C. The miniaturization technologies: past, present, and future. IEEE Trans. Industr. Electron. 42, 423–430 (1995).
Google 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).
Google 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).
Google Scholar
Rault, T., Bouabdallah, A. & Challal, Y. Energy efficiency in wireless sensor networks: a top-down survey. Comput. Netw. 67, 104–122 (2014).
Google Scholar
Sun, H. et al. MEMS based energy harvesting for the Internet of Things: a survey. Microsyst. Technol. 24, 2853–2869 (2018).
Google 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); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”113.”>
Saif, I. & Ammanath, B. ‘Trustworthy AI’ is a framework to help manage unique risk. MIT Technology Review / (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 SSRN (2022).
Advancing Accountability in AI: Governing and Managing Risks throughout the Lifecycle for Trustworthy AI OECD Digital Economy Papers Vol. 349 (OECD, 2023); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”116.”>
Baquero, J. A., Burkhardt, R., Govindarajan, A. & Wallace, T. Derisking AI: Risk Management in AI Development (McKinsey, 2020).
AI and Risk Management (Deloitte, 2018); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”118.”>
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).
Google 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); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”123.”>
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).
Google 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).
Google 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).
Google 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); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”133.”>
Ridzuan, F. & Zainon, W. M. N. W. A review on data cleansing methods for big data. Procedia Comput. Sci. 161, 731–738 (2019).
Google 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).
Elmenreich, W. An Introduction to Sensor Fusion (Vienna Univ. Technology, 2002).
Sweeney, L. Simple demographics often identify people uniquely. Health 671, 1–34 (2000).
Barocas, S. & Nissenbaum, H. Big data’s end run around procedural privacy protections. Commun. ACM 57, 31–33 (2014).
Google 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).
Google Scholar
Dhar, P. The carbon impact of artificial intelligence. Nat. Mach. Intell. 2, 423–425 (2020).
Google Scholar
Wu, C. J. et al. Sustainable AI: environmental implications, challenges and opportunities. Proc. Mach. Learn. Syst. 4, 795–813 (2022).
Google Scholar
Van Wynsberghe, A. Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics 1, 213–218 (2021).
Google Scholar
Lannelongue, L., Grealey, J. & Inouye, M. Green algorithms: quantifying the carbon footprint of computation. Adv. Sci. 8, 2100707 (2021).
Google Scholar
Cooper, Z. G. T. Of dog kennels, magnets, and hard drives: dealing with big data peripheries. Big Data Soc. 8, 20539517211015430 (2021).
Google Scholar
Bridges, L. E. Material entanglements of community surveillance & infrastructural power. AoIR Selected Papers of Internet Research, 2020 (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).
Google Scholar
Sorrell, S. Jevons’ paradox revisited: the evidence for backfire from improved energy efficiency. Energy Policy 37, 1456–1469 (2009).
Google Scholar
Verbeek, P. P. Ambient intelligence and persuasive technology: the blurring boundaries between human and technology. Nanoethics. 3, 231–242 (2009).
Google 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).
Google 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); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”154.”>
Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).
Google Scholar
Soltoggio, A. et al. A collective AI via lifelong learning and sharing at the edge. Nat. Mach. Intell. 6, 251–264 (2024).
Google Scholar
Algorithmic Impact Assessment: A Case Study in Healthcare (Ada Lovelace Institute, 2022); class=”c-article-references__item js-c-reading-companion-references-item” data-counter=”157.”>
Metcalf, J. et al. A relationship and not a thing: a relational approach to algorithmic accountability and assessment documentation. Preprint at (2022).
Lavin, A. et al. Technology readiness levels for machine learning systems. Nat. Commun. 13, 6039 (2022).
Google Scholar
Huckelberry, J. et al. TinyML security: exploring vulnerabilities in resource-constrained machine learning systems. Preprint (2024).
Baxter, G. & Sommerville, I. Socio-technical systems: from design methods to systems engineering. Interact. Comput. 23, 4–17 (2011).
Google 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. Zenodo (2019).
Abbas, R., Pitt, J. & Michael, K. Socio-technical design for public interest technology. IEEE Trans. Technol. Soc. 2, 55–61 (2021).
Google 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).
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